Recently, Cloud-based Mobile Augmentation (CMA) approaches have gained remarkable ground from academia and industry. CMA is the state-of-the-art mobile augmentation model that employs resource-rich clouds to increase, enhance, and optimize computing capabilities of mobile devices aiming at execution of resource-intensive mobile applications. Augmented mobile devices envision to perform extensive computations and to store big data beyond their intrinsic capabilities with least footprint and vulnerability. Researchers utilize varied cloud-based computing resources (e.g., distant clouds and nearby mobile nodes) to meet various computing requirements of mobile users. However, employing cloud-based computing resources is not a straightforward panacea. Comprehending critical factors (e.g., current state of mobile client and remote resources) that impact on augmentation process and optimum selection of cloud-based resource types are some challenges that hinder CMA adaptability. This paper comprehensively surveys the mobile augmentation domain and presents taxonomy of CMA approaches. The objectives of this study is to highlight the effects of remote resources on the quality and reliability of augmentation processes and discuss the challenges and opportunities of employing varied cloud-based resources in augmenting mobile devices. We present augmentation definition, motivation, and taxonomy of augmentation types, including traditional and cloud-based. We critically analyze the state-of-the-art CMA approaches and classify them into four groups of distant fixed, proximate fixed, proximate mobile, and hybrid to present a taxonomy. Vital decision making and performance limitation factors that influence on the adoption of CMA approaches are introduced and an exemplary decision making flowchart for future CMA approaches are presented. Impacts of CMA approaches on mobile computing is discussed and open challenges are presented as the future research directions.

Transcript

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IEEE COMMUNICATIONS SURVEYS & TUTORIALS, ACCEPTED FOR PUBLICATION 1
Cloud-Based Augmentation for Mobile Devices:
Motivation, Taxonomies, and Open Challenges
Saeid Abolfazli, Member, IEEE, Zohreh Sanaei, Member, IEEE, Ejaz Ahmed, Member, IEEE, Abdullah
Gani, Senior Member, IEEE, Rajkumar Buyya, Senior Member, IEEE
Abstract—Recently, Cloud-based Mobile Augmentation (CMA)
approaches have gained remarkable ground from academia and
industry. CMA is the state-of-the-art mobile augmentation model
that employs resource-rich clouds to increase, enhance, and
optimize computing capabilities of mobile devices aiming at
execution of resource-intensive mobile applications. Augmented
mobile devices envision to perform extensive computations and
to store big data beyond their intrinsic capabilities with least
footprint and vulnerability. Researchers utilize varied cloud-
based computing resources (e.g., distant clouds and nearby
mobile nodes) to meet various computing requirements of mobile
users. However, employing cloud-based computing resources is
not a straightforward panacea. Comprehending critical factors
(e.g., current state of mobile client and remote resources) that
impact on augmentation process and optimum selection of cloud-
based resource types are some challenges that hinder CMA
adaptability. This paper comprehensively surveys the mobile aug-
mentation domain and presents taxonomy of CMA approaches.
The objectives of this study is to highlight the effects of remote
resources on the quality and reliability of augmentation processes
and discuss the challenges and opportunities of employing varied
cloud-based resources in augmenting mobile devices. We present
augmentation deﬁnition, motivation, and taxonomy of augmen-
tation types, including traditional and cloud-based. We critically
analyze the state-of-the-art CMA approaches and classify them
into four groups of distant ﬁxed, proximate ﬁxed, proximate
mobile, and hybrid to present a taxonomy. Vital decision making
and performance limitation factors that inﬂuence on the adoption
of CMA approaches are introduced and an exemplary decision
making ﬂowchart for future CMA approaches are presented. Im-
pacts of CMA approaches on mobile computing is discussed and
open challenges are presented as the future research directions.
Index Terms—Cloud-based Mobile Augmentation, Mobile
Cloud Computing, Cloud Computing, Resource-intensive Mobile
Application, Computation Ofﬂoading, Resource Outsourcing.
I. INTRODUCTION
SINCE a decade ago, the visions of ‘information under
ﬁngertip’ [1] and ‘unrestricted mobile computing’ [2]
aroused the need to enhance computing power of mobile
devices to meet the insatiable computing demands of mobile
users [3]. In the late 90s, the concept of load sharing and
Manuscript received Dec 18, 2012; revised March 05, 2013 and 06
May, 2013;This work is funded by the Malaysian Ministry of Higher
Education under the University of Malaya High Impact Research Grant -
UM.C/HIR/MOHE/FCSIT/03. Ejaz Ahmed’s research work is supported by
the Bright Spark Unit, University of Malaya, Malaysia.
Saeid Abolfazli(corresponding author), Zohreh Sanaei, Ejaz Ahmed, and
Abdullah Gani are with the Department of Computer System & Technology,
The University of Malaya, Kuala Lumpur, Malaysia (e-mail: {abolfazli,sanaei,
ejazahmed}@ieee.org; abdullah@um.edu.my)
RajKumar Buyya is with the Department of Computing and Information
Systems, The University of Melbourne, 111, Barry Street, Carlton, Melbourne,
VIC 3053, Australia, Email: raj@csse.unimelb.edu.au
remote execution aimed to augment computing capabilities of
mobile devices by shifting the resource-intensive mobile codes
to surrogates (powerful computing device(s) in vicinity) [4]–
[6]. Although remote execution efforts [7]–[18] have yielded
many impressive achievements, several challenges such as
reliability, security, and elasticity of surrogates hinder the
remote execution adaptability [19]. For instance, the resource
sharing and computing services of surrogates can be termi-
nated without prior notice and their content can be accessed
and altered by the surrogate machine or other users in the
absence of a Service Level Agreement (SLA). SLA is a formal
contract employed and negotiated in advance between service
provider and consumer to enforce certain level of quality
against a fee.
Few years later, emergence of cloud resources created an
opportunity to mitigate the shortcomings of utilizing surro-
gates in augmenting mobile devices. Cloud is a type of dis-
tributed system comprised of a cluster of powerful computers
accessible as uniﬁed computing resource(s) based on an SLA
[20]. Cloud computing as “a model for enabling ubiquitous,
convenient, on-demand network access to a shared pool of con-
ﬁgurable computing resources (e.g., networks, servers, storage,
applications, and services) that can be rapidly provisioned
and released with minimal management effort or service or
service provider interaction” [21] stimulates researchers to
adopt the cutting edge technology in mobile device augmenta-
tion: Cloud-based Mobile Augmentation (CMA). Cloud-based
Mobile Augmentation (CMA) is the-state-of-the-art mobile
augmentation model that leverages cloud computing technolo-
gies and principles to increase, enhance, and optimize com-
puting capabilities of mobile devices by executing resource-
intensive mobile application components in the resource-rich
cloud-based resources. Cloud-based resources include varied
types of mobile/immobile computing devices that follow cloud
computing principles [22], [23] to perform computations on
behalf of the resource-constraint mobile devices. Figure 1
depicts major building blocks of a typical CMA system. It
is notable that these building blocks are optional superset, and
speciﬁc CMA system may not have all these building blocks.
CMA efforts [24]–[27], [27]–[49] exploit various cloud-
based computing resources, especially distant clouds and prox-
imate mobile nodes to augment mobile devices. Distant clouds
are giant clouds such as Amazon EC21
located inside the
vendor premise —far away from the mobile clients—offering
inﬁnite, elastic computing resources with extreme computing
1http://aws.amazon.com/ec2/

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IEEE COMMUNICATIONS SURVEYS & TUTORIALS, ACCEPTED FOR PUBLICATION 2
Fig. 1. Major Building Blocks of an Exemplary CMA System.
power and high WAN (Wide Area Network) latency. Proximate
mobile nodes are building a cluster of mobile computing de-
vices scattered near the mobile clients offer limited computing
power with lower WAN latency than distant clouds.
Although heterogeneity among cloud-based resources in-
creases service ﬂexibility and enhances users’ computing expe-
rience, determining the most appropriate computing resources
among available options and performing upfront analysis of
inﬂuential factors (e.g., user preferences and available native
mobile resources) are critical in the adaptability of CMA
approaches. Thus, ‘resource scheduler’ and ‘analyzer and
optimizer’ components depicted in Figure 1 are needed to
analyze and allocate appropriate resources to each task in
a typical CMA system. Moreover, several questions need to
be addressed before the CMA concept can be successfully
employed in the real scenarios. For instance, can CMA aug-
ment computing capabilities of mobile devices and save local
resources to enhance user experience? Is CMA always feasible
and beneﬁcial? Which type of resources is appropriate for a
certain task? Answering these questions requires ‘monitoring
and proﬁler’, ‘QoS management’, ‘context management’, and
‘decision making engine’ components to perform in each
CMA system (see Figure 1). Therefore, an augmentation deci-
sion engine similar to those used in [25], [33], [49] and exem-
plary decision making ﬂow presented in this paper (discussed
in part VI-C) to determine the mobile augmentation feasibility
is needed to amend the CMA performance and reliability.
During augmentation process, the local and native application
state stack needs synchronization to ensure integrity between
native and remote data. Upon successful outsourcing, remote
results need to be returned and integrated to the source mobile
device. Thus, the ‘Synchronizer’ component needs to perform
in typical CMA approaches (see Figure 1).
Although CMA approaches can empower mobile process-
ing and storage capabilities, several disadvantages such as
application development complexity and unauthorized access
to remote data demand a systematized plenary solution.
Performance of the CMA systems is highly inﬂuenced by
various challenges and issues of wireless networking and
cloud computing technologies. CMA researchers require a
high performance, elastic, robust, reliable, and foreseeable
communication throughput between mobile nodes and cloud
servers which is not yet realized despite of remarkable efforts
and achievements of communication and networking societies.
Current shortcomings and deﬁciencies of wireless communi-
cation and networking, especially seamless connectivity and
mobility, high performance communication throughput pro-
visioning, and wireless data interception discourage system
analysts, engineers, developers, and entrepreneurs from de-
ploying CMA-enabled mobile applications due to the high risk
of system malfunction and user experience degradation.
Moreover, CMA systems require accurate estimation mech-
anisms to predict the overall time and energy consumption
of communication and computation tasks while exploiting
clouds. Such estimation is a challenging task considering
huge infrastructures’ performance diversity [50] and policy
heterogeneity [51] of cloud services in intermittent wireless
environment. Despite of blooming efforts endeavoring to ana-
lyze and comprehend the cloud computing model and behavior
[52]–[55], CMA solutions are still unable to accurately fore-
see required time and energy of exploiting cloud resources
to execute intensive applications. Additionally, sundry cloud
challenges, especially live VM migration, infrastructure and
platform heterogeneity, efﬁcient allocation of clouds to tasks,
QoS management, security, privacy, and trust in cloud increase
system complexity and decrease successful CMA systems
adoption.
Among limited studies of the domain, [19] and [56] survey
remote execution and application ofﬂoading algorithms with
focus on how task ofﬂoading is performed in various efforts.
Fernando et al. [57] and Dinh et al. [58] sought to explain the
convergence of mobile and cloud computing, and distinguish
it from the earlier domains such as cloud and grid computing
[59]. The authors describe issues, particularly mobile applica-
tion ofﬂoading, privacy and security, context awareness, and
data management. Sanaei, Abolfazli, Gani, and Buyya [51]
present a comprehensive survey on MCC with major focus
on heterogeneity. The authors describe the challenges and
opportunities imposed by heterogeneity and identify hardware,
platform, feature, API, and network as the roots of MCC

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IEEE COMMUNICATIONS SURVEYS & TUTORIALS, ACCEPTED FOR PUBLICATION 3
TABLE I
LIST OF ACRONYMS AND CORRESPONDING FULL FORMS.
Acronym Full form
2D 2 Dimensional
2G 2nd Generation
3D 3 Dimensional
3G 3rd Generation
API Application Programming Interface
App Application (mobile application)
ARM Advanced RISC Machines
CMA Cloud-based Mobile Augmentation
CPU Central Processing Unit
DSL Domain Speciﬁc Language
DVMS Dynamic VM Synthesis
FTP File Transfer Protocol
GPU Graphics Processing Unit
GUI Graphical User Interface
I/O Input/Output
IaaS Infrastructure as a Service
IP Internet Protocol
IP TV Internet Protocol Television
iSCSI Internet Small Computer System Interface
MCC Mobile Cloud Computing
MNO Mobile Network Operator
OS Operating System
P2P Peer-to-Peer
PC Personal Computer
QoS Quality of Service
R&D Research and Development
RAM Random Access Memory
RISC Reduced Instruction Set Computing
RPC Remote Procedure Call
SAL Service Abstraction Layer
SLA Service Level Agreement
TCP Transmission Control Protocol
UDDI Universal Description Discovery and Integration
UI User Interface
VM Virtual Machine
WAN Wide Area Network
Wi-Fi Wireless Fidelity
heterogeneity. They explain major heterogeneity handling ap-
proaches, particularly virtualization, service oriented archi-
tecture, and semantic technology. However, the computing
performance, distance, elasticity, availability, reliability, and
multi-tenancy of remote resources are marginally discussed
in these studies that necessitate further research to explain
the impact of remote resources on augmentation process and
highlight paradigm shift from the unreliable surrogates to
reliable clouds.
In this paper, we survey the state-of-the-art mobile augmen-
tation efforts that employ cloud computing infrastructures to
enhance computing capabilities of resource-constraint mobile
devices, especially smartphones. To the best of our knowledge,
this is the ﬁrst effort that studies the impacts of cloud-based
computing resources on mobile augmentation process. We dif-
ferentiate augmentation from similar concepts of load sharing
and remote execution, and present augmentation motivation.
We review efforts that endeavor to mitigate the mobile devices’
shortcomings and classify them as hardware and software to
devise a taxonomy. The impacts of CMA in mobile comput-
ing are presented. The characteristics of cloud-based remote
resources and their role in CMA effectiveness are studied and
classiﬁed into four groups, namely distant immobile clouds,
proximate immobile computing entities, proximate mobile
computing entities, and hybrid based on their mobility and
physical location traits. Furthermore, the state-of-the-art CMA
models are reviewed and taxonomized into four classes of
distant ﬁxed, proximate ﬁxed, proximate mobile, and hybrid
according to our cloud-based resource classiﬁcation. Factors
impact on the CMA adaptability are identiﬁed and described as
augmentation environment, user preferences and requirements,
mobile devices, cloud servers, and contents. Five major metrics
that limit the performance of CMA approaches are studied. A
sample ﬂowchart of decision making engines for imminent
CMA solutions is presented and several open challenges are
discussed as the future research directions. Such survey is
beneﬁcial to the communication and networking communities,
because comprehending CMA process and current deploy-
ment challenges are beneﬁcial in modifying the fundamental
networking infrastructures to optimize the CMA process. In
this paper, we use the terms mobile devices and smartphones
interchangeably with similar notion. Table I shows the list of
acronyms used in the paper.
The remainder of this paper is organized as follows. Section
II introduces mobile computation augmentation, presents its
motivation and describes the taxonomy of mobile augmenta-
tion types. The impacts of CMA on mobile computing are
presented in Section III. Section IV presents the analysis
and taxonomy of varied cloud-based augmentation resources.
Comprehensive survey of the state-of-the-art CMA approaches
is presented and taxonomy is devised in Section V. We
discuss the CMA decision making and limitation factors and
illustrate CMA feasibility in Section VI. Finally, open research
challenges are presented in Section VII and paper is concluded
in Section VIII.
II. MOBILE COMPUTATION AUGMENTATION
In this Section, we present a deﬁnition on mobile computing
augmentation based on the available deﬁnitions on the relevant
concepts, particularly remote execution [5] and cyber foraging
[6]. Additionally, the motivation for performing mobile com-
putation augmentation is described and taxonomy of mobile
augmentation types is presented.

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IEEE COMMUNICATIONS SURVEYS & TUTORIALS, ACCEPTED FOR PUBLICATION 4
TABLE II
INITIAL FEATURES OF MOBILE EMPOWERMENT APPROACHES.
Approach Architecture Client Load Migration Partitioning Server Mobility
Load Sharing Client-Server Entire Task Entire task NA Server NA
Remote
Client-Server Entire Task Entire/partial Static
Server No
Execution /desktop
Cyber Client-Server
Entire Task Entire/partial Dynamic
Surrogates No
Foraging Peer-to-Peer
Mobile Varies, e.g.,
/Nil
Entire/partial/ Static & Server, Yes
Computation Client-server Entire/partial Nil migration dynamic surrogate
Augmentation P2P, Adhoc (Use remote &mobile
collaborative services)
A. Deﬁnition
Indeed, empowering computation capabilities of mobile
devices is not a new concept and there have been different
approaches to achieve this goal, including load sharing [4],
remote execution [5], cyber foraging [6], and computation
ofﬂoading [60], [61] that are described as follows. We have
analyzed them and summarized the analysis results in Table
II. Results in this Table are extracted from the early efforts in
each category, which are already deviated from their original
characteristics due to the research achievements.
• Load Sharing: Othman and Hailes’ work [4] in 1998
can be considered as one of the earliest efforts to conserve
native resources of mobile devices using a software approach.
The main idea is inspired from the concept of load balancing
in distributed computing that is “a strategy which attempts
to share loads in a distributed system without attempting
to equalize its load” [4]. This approach migrates the whole
computation job for remote execution. It considers several
metrics such as job size, available bandwidth, and result size
to identify if the load balancing and transferring the job to
the remote computer can save energy. However, they need to
send the task and data to the nearest base station and wait for
the results to return. The base station is responsible to ﬁnd
appropriate server to run the job and forward the results back
to the mobile device. Moreover, computing server is a ﬁxed
computer and there is no provision for user and code mobility
at run time.
• Remote Execution: The concept of remote execution for
mobile clients emerged in 90s and several researchers [5],
[62]–[65] endeavor to enable mobile computers to performing
remote computation and data storage to conserve their scarce
native resources and battery. In 1998 [5], feasibility of remote
execution concept on mobile computers, particularly laptops
was investigated. The authors report that remote execution can
save energy if the remote processing cost is lower than local
execution. Remote execution involves migrating computing
tasks from the mobile device to the server prior the execution.
The server performs the task and sends back the results to
the mobile device. However, difference between computation
power of client and server is not a metric of decision making
in this method. Moreover, the whole task needs to be migrated
to the remote server prior the execution which is an expensive
effort. It also neglects the impact of environment characteris-
tics on the remote execution outcome. Static decision making
is another shortcoming of this proposal.
• Cyber Foraging: Satyanarayana in 2001 [6] further
developed the remote execution idea by considering dynamism
in remote execution process. The author deﬁned cyber forag-
ing as the process “to dynamically augment the computing
resources of a wireless mobile computer by exploiting wired
hardware infrastructure”. Resources in cyber foraging are
stationary computers or servers in public places —connected
to wired Internet and power cable—that are willing to perform
intensive computation on behalf of the resource-constraint
mobile devices in vicinity.
However, load sharing, remote execution, and cyber forag-
ing approaches assume that the whole computing task is stored
in the device and hence, it requires the intensive code and data
to be identiﬁed and partitioned for ofﬂoading —either stati-
cally prior the execution or dynamically at runtime —which
impose large overhead on resource-poor mobile device [19].
Moreover, as Kumar et al. [66] explain, for each mobile
user that runs the intensive application, the whole ofﬂoading
process must be repeated including decision making process
in the device and transferring the heavy components and large
data to the network. Due to slight differences among these
concepts, researchers use the terms ‘remote execution’, ‘cyber
foraging’, and ‘computation ofﬂoading’ interchangeably in the
literature with similar principle and notion.
Nevertheless, researchers in recent activities [36], [42], [45],
[46] aim to enhance computing capabilities of mobile devices
in a slightly different manner. They assume to store the
intensive code and data outside the device and keep the rest in
the mobile device instead of storing the whole task —including
both lightweight and intensive code and data —in the mobile
device. Therefore, the overhead of identifying, partitioning,
and migrating the resource-intensive task is mitigated, energy
is saved, and storage problem is alleviated in mobile devices.
Moreover, storing intensive components outside the device,
in a publicly accessible storage, can increase their reusability
and enable more than one user to leverage their computation
services. Therefore, we coin the term mobile computation
augmentation as the wider phrase that subsumes load sharing,
remote execution, cyber foraging, and other approaches that

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IEEE COMMUNICATIONS SURVEYS & TUTORIALS, ACCEPTED FOR PUBLICATION 5
augment computing capabilities of mobile devices.
• Mobile Computation Augmentation: Mobile computa-
tion augmentation, or augmentation in brief, is the process of
increasing, enhancing, and optimizing computing capabilities
of mobile devices by leveraging varied feasible approaches,
hardware and software. Mobile device is any non-stationary,
battery-operating computing entity able to interact with end-
user and execute transactions, store data, and communicate
with the environment using wireless technologies and varied
sensors. Smartphone, Tablet, handheld/wearable computing
devices, and vehicle mount computers are mobile device in-
stances. Approaches that can augment mobile devices include
hardware and software. Hardware approach involves manu-
facturing high-end physical components, particularly CPU,
memory, storage, and battery. Software approaches can be
—but are not limited to —computation ofﬂoading, remote data
storage, wireless communication, resource-aware computing,
ﬁdelity adaptation, and remote service request (e.g., context
acquisition).
Augmentation approaches can increase computing capabil-
ities of mobile devices and conserve energy. They can be
leveraged in three main categories of applications, namely (i)
computing-intensive software such as speech recognition and
natural language processing, (ii) data-intensive programs such
as enterprise applications, and (iii) communication-intensive
applications such as online video streaming applications. The
augmented mobile device is able to perform complex tasks that
could not otherwise perform. Hence, the mobile application
developers do not take into account resource shortcomings
of mobile devices in developing mobile application and users
will not consider their devices weaknesses in utilizing varied
complex applications.
B. Motivation
Mobile devices have recently gained momentous ground in
several communities like governmental agencies, enterprises,
social service providers (e.g., insurance, Police, ﬁre depart-
ments), healthcare, education, and engineering organizations
[67], [68]. However, despite of signiﬁcant improvement in
mobiles’ computing capabilities, still computing requirements
of mobile users, especially enterprise users, is not achieved.
Several intrinsic deﬁciencies of mobile devices encumber
feasibility of intense mobile computing and motivate aug-
mentation. Leveraging augmentation approaches, vision of
performing intense mobile operations and control such as
remote surgery, on-site engineering, and visionary scenarios
similar to the lost child and disaster relief described in [69]
will become reality. In this part, we analyze and taxonomize
smartphones’ deﬁcits that can be alleviated by augmentation.
Figure 2 depicts our devised taxonomy.
1) Processing Power: Processing deﬁciencies of mobile
clients due to slow processing speed and limited RAM is one
of the major challenges in mobile computing [69]. Mobile de-
vices are expected to have high processing capabilities similar
to computing capabilities of desktop machines for performing
computing-intensive tasks to enrich user experience. Realizing
such vision requires powerful processor being able to perform
large number of transactions in a short course of time.
Large internal memory/RAM to store state stack of all
running applications and background services is also lacking.
Beside local memory limitations, memory leakage also inten-
siﬁes memory restrains of mobile devices. Memory leakage
is the state of memory cells being unnecessarily occupied by
running applications and services or those cells that are not
released after utilization. Garbage-collector-based languages
like Java in Android2
contribute to memory leakage due to
failed or delayed removal of unused objects from the memory
[70]. Android’s kernel level transactions can also leak memory
in the absence of memory management mechanisms [70],
[71]. Moreover, inward deﬁciency and inefﬁcient design and
implementation of mobile applications can also waste scarce
memory of mobile devices. Thus, in the absence of required
memory, applications are frequently paused or terminated
by the operating system leading to longer execution time,
excessive resource dissipation, and ultimately mobile user
experience degradation.
2) Energy Resources: Energy is the only non-replenishable
resource in mobile devices that demands external resources
to be replenished [72], [73]. Currently, energy requirement of
a mobile device is supplied via lithium-ion battery that lasts
only few hours if device is computationally engaged. Battery
capacity is increasing at about 5 to 10% a year [74], [75]
as battery cells are excessively dense [72]. Moreover, mo-
bile device manufacturers endeavor to attain device lightness,
compactness, and handiness, which prevent exploitation of
bulky long-lasting batteries. User safety is another concern that
conﬁnes manufacturers to produce low capacity batteries [76].
While explosion of a battery with few hundreds milliamperes
capacity can jeopardize human life [77], explosion of a high-
capacity battery can carry catastrophic consequences.
Energy harvesting efforts [78]–[80] seek to replenish energy
from renewable resources, particularly human movement, solar
energy, and wireless radiation, but these resources are mostly
intermittent and not available on-demand [81]. For instance,
a sitting mobile user at night cannot have any external power
source in the absence of wall power and wireless radiations.
Moreover, researchers aim at reducing the energy overhead
in different aspects of computing, including hardware, OS,
application, and networking interface [82], [83]. Efforts are di-
rected to develop alternative energy resources such as nuclear
batteries that will likely last months or years [84]. However,
signiﬁcant deal of R&D is needed to fulﬁll ever-increasing
energy requirements of mobile users.
Hence, in the absence of long-spanning energy on mobile
devices, alternative augmentation approaches play a vital role
in maturing mobile and ubiquitous computing.
3) Local Storage: Drastic increasing in the number of
applications and amount of digital contents such as pictures,
songs, movies, and home ﬁlms [85] from one hand and limited
storage of mobile devices from the other hand decelerate
usability of mobile devices. While PCs are able to locally
store huge amount of data, smartphones are limited to few
gigabytes of space which are mostly occupied by system
ﬁles, user applications, and personal data. Therefore, frequent
2urlhttp://www.android.com/

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IEEE COMMUNICATIONS SURVEYS & TUTORIALS, ACCEPTED FOR PUBLICATION 6
Fig. 2. Taxonomy of Augmentation Motivation: Intrinsic and non-intrinsic mobile challenges motivate augmentation.
storing, updating, and deleting data as well as uninstalling and
reinstalling applications due to space limitation cause irksome
impediments to mobile users [86]. Additionally, delivering
ofﬂine usability, which is one of the most important character-
istics of contemporary applications, remains an open challenge
since mobile devices lack large local storage.
4) Visualization Capabilities: Effective data visualization
on small mobile devices’ screen is a non-trivial task when
current screen manufacturing technologies and energy limita-
tions do not allow signiﬁcant size extensions without losing
device handiness. Currently smartphones like HTC One X3
and Samsung Galaxy Note II4
have the biggest screens, at
4.7 and 5.5 inches respectively; however, they are very small
compared to PCs and notebooks.
Therefore, efﬁcient data visualization in small smartphones’
screen necessitates software-based techniques similar to tab-
ular pages, 3D objects, multiple desktops, switching between
landscape and portrait views (needs accelerometer), and verbal
communication to virtually increase presentation area. Re-
cently, computing-intensive mobile 3D display technology is
promising to noticeably mitigate the visualization deﬁcit of
contemporary smartphones. Glass-free auto-stereoscopic dis-
plays [87] can present 3D data by exploiting binocular parallax
to offer a different view for each eye. Taking advantages
of current and imminent software-based techniques beside
native tools, especially tilting sensors signiﬁcantly improve the
mobile visualization capabilities in the near future. However,
these approaches are computation-intensive processes that
quickly drain battery [87], [88]. A feasible alternative solution
to realize software-based content presentation approaches is to
augment smartphones’ computing capabilities.
5) Security, Privacy, and Data Safety: Mobile end-users
are concerned about security and privacy of their personal
data, banking records, and online behaviors [89]. The dramatic
increase in cybercrime and security threats within mobile
devices [90], cloud resources [91] and wireless transactions
makes security and privacy more challenging than ever [92].
Moreover, performing complex cryptographic algorithms is
likely infeasible because of computing deﬁciencies of mobile
3http://www.htc.com/www/smartphones/htc-one-x/
4http://www.samsung.com/my/consumer/mobile-devices/galaxy-
note/galaxy-note/GT-N7100RWDXME
devices. Securing ﬁles using pair of credentials is also less
realistic in the absence of large keyboard.
Data safety is another challenge of mobile devices, because
information stored inside the local storage of mobile devices
are susceptible to safety breaches due to high probability of
hardware malfunction, physical damage, stealing, and loss.
Amalgam of these problems and deﬁciencies in mobile
computing stimulates researchers from academia and industry
to exploit novel technologies and approaches to augment
computing capabilities of mobile devices which is subject of
this study.
C. Mobile Augmentation Types: Taxonomy
In this Section, we analyze and classify augmentation ap-
proaches into two major types of hardware and software. Our
devised taxonomy is depicted in Figure 3 and described as
follows.
Hardware. The hardware approach aims to empower smart-
phones by exploiting powerful resources, particularly multi-
core CPUs with high clock speed [93], large screen, and long-
lasting battery [84], [94]. ARM5
and Samsung6
are major mo-
bile processor manufacturers producing multi-core processors
such as ARM Cortex-597
and Samsung Exynos 5 Octa core8
that perform in higher speed than a single core processors
[93]. However, doubling the CPU clock speed approximately
octuples the device energy consumption [66].
Nevertheless, augmentation via sophisticated hardware is
hindered by several obstacles. Firstly, generating powerful
processor, large storage, and big screen decrease smartphone
handiness due to additional heat, size, and weight. Secondly,
considering the fact that utilizing long-lasting battery in small
mobile devices is not feasible with current technologies, re-
source enlargement contributes toward faster battery drainage
and shorter battery life time. Thirdly, equipping mobile de-
vices with high-end hardware noticeably increases their price
5http://arm.com
6http://samsung.com
7http://www.arm.com/products/processors/cortex-a50/cortex-a57-
processor.php
8http://www.samsung.com/global/business/semiconductor/minisite/Exynos/
blog CES 2013 Samsung Mobilizes Possibility with Exynos5Octa.html

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IEEE COMMUNICATIONS SURVEYS & TUTORIALS, ACCEPTED FOR PUBLICATION 7
Fig. 3. Taxonomy of Mobile Augmentation Types.
compare to the stationary machines. Unlike PCs, smartphone’s
hardware is not upgradable; hence, a new device should
be possessed in case of technology advancement. Therefore,
in the absence of futuristic engineering technologies, the
hardware-based augmentation process is slow and expensive
that necessitates alternative augmentation approaches to en-
hance computing capabilities of mobile devices without drastic
ownership price hike.
Software. Software-oriented mobile augmentation approaches
are classiﬁed into ﬁve groups and will be explained later in
this part. Resources that are used in major software-oriented
approaches are classiﬁed into two groups, namely traditional
and cloud-based. Their major differences lie on resource pro-
visioning and access strategies, service security and delivery
models, and resource characteristics. In traditional approaches,
researchers leverage centralized resources of distant traditional
servers or free nearby surrogates. Several problems such
as resource availability, elasticity, and security of traditional
approaches hinder their success. For instance, surrogates can
terminate their services anytime without considering their
current load, and can violate user security and privacy by
changing execution sequence or altering raw and processed
data.
To alleviate the problems of traditional servers, researchers
in recent efforts [25], [27], [29], [31], [33]–[35], [41], [43],
[44], [95] exploit highly available, elastic, secure cloud in-
frastructure. “Cloud is a type of parallel and distributed
system consisting of a collection of interconnected and vir-
tualized computers dynamically provisioned and presented as
one or more uniﬁed computing resources based on service-
level agreements established through negotiation between the
service provider and consumers” [20].
While utilizing cloud resources, users pay for the amount
and duration they utilize various resources (e.g., CPU, mem-
ory, and bandwidth) based on an agreed SLA. In the SLA, the
amount and quality of required resources such as processor,
RAM, and storage are speciﬁed and user is billed accordingly.
Service delivery failure will be compensated by the vendor.
Lucrative ﬁnancial beneﬁts of cloud services encourage cloud
providers to compete in delivering high service availability,
reliability, security, and robustness to increase their market
share. Hence, the augmentation performance is less affected
by resource unreliability and interruption.
Moreover, cloud infrastructures are available to end-users
via Virtual Machine(VM)9
to increase resource utilization
ratio and enhance overall security and privacy. Virtualization
technology aims to enable resource sharing in an isolated
environment called VM. It realizes execution of multiple
operating systems on a single machine and enables sharing
of large resources among multiple end-users. Users can only
access to infrastructures allocated to their VMs and cannot
access prohibited resources and contents.
Table III summarizes the comparison results of traditional
and cloud-based resources and advocates differentiations be-
tween the conventional servers and clouds. High computing
power, elasticity, mobility support, low utilization overhead,
and security are some of the signiﬁcant advantages of cloud
resources compare to the surrogates that advocate the latest
paradigm shift in mobile augmentation.
Software augmentation techniques are classiﬁed as remote
execution (ofﬂoading or cyber foraging) [5]–[8], [10]–[13],
[16]–[18], [25], [29], [30], [33]–[35], [41], [43], [44], [96],
remote storage [97], Multi-tier programming [36], [45], [46],
live cloud-streaming [98], resource-aware computing [99],
[100], and ﬁdelity adaptation [101] and explained as follows.
• Remote execution: As explained in II-A,the resource-
hungry components of mobile applications —in whole or
part —are migrated to the resource-rich computing device(s)
that are willing to share their resources with mobile devices.
Rapid development of heterogeneous mobile devices obliges
adaptive ofﬂoading approaches able to enhance capabilities
of wide range of mobile devices in dynamic environment
with least processing overhead and latency. The efﬁciency of
ofﬂoading approaches highly depends on what component(s)
can be partitioned? When partitioning takes place? Where to
execute the component(s)? And how to communicate with the
remote server? [102]. Ofﬂoading approaches perform varied-
time analysis to answer these questions, which are classiﬁed
into three groups and explained as below.
Design Time Analysis: In this method, the application’s
complexity is analyzed at design time to determine the answer
of four above questions. Application developer or a middle-
ware speciﬁes the resource-intensive components of mobile
9http://www.vmware.com/virtualization/what-is-virtualization.html

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IEEE COMMUNICATIONS SURVEYS & TUTORIALS, ACCEPTED FOR PUBLICATION 8
TABLE III
COMPARISON BETWEEN TRADITIONAL AND CLOUD-BASED COMPUTING RESOURCE.
Features Traditional Cloud-Based
Computation Power Low High
Elasticity Low High
User Experience Low High
Reliability Low High
Availability Intermittent On-demand
Client Mobility Limited Unlimited
Multi-tenancy Not available Available
Serving Incentive Not provisioned Provisioned
Utilization Cost Free Pay-As-You-Use
Utilization Overhead High Low
Management Decentralized De/Centralized
Back-end Connectivity Wired Wired & Wireless
Communication Latency Low Varied
Computation Latency High Low
Security Low High
Data Safety Low High
application that can be ofﬂoaded to the remote server and label
them as remote component(s). Programmers decide how to
partition application and adapt its performance to the dynamic
mobile environment which is a non-trivial task, mainly due
to the lack of knowledge about the execution environment.
Performing such action needs excessive programming skill
and knowledge of computation ofﬂoading. Design time ap-
proaches [8], [10], [12] notably save native resources of
mobile device by reducing the processing and monitoring
overheads. However, partitioning prior to the execution is not
always optimal and cannot accurately adapt performance in
diverse execution environments and also imposes extra efforts
on the application developer or middleware for deciding on
partitioning. Hence, design time partitioning approaches are
likely become obsolete.
Runtime Analysis: Runtime or dynamic partitioning referred
to methods such as [25], [103] that aims to answer four
questions at runtime. They identify and partition the resource-
hungry parts of the application, specify how and where to
execute the partitioned components [102], [104], and de-
termine how to communicate with the server. In dynamic
methods, resource requirement of the application is analyzed
and available resources are detected to decide if the appli-
cation requires remote resources. Upon decision making the
system performs ofﬂoading. Further monitoring is necessary
to gather knowledge of available remote resources to maintain
execution history. Although these approaches provide dynamic
and ﬂexible solutions, large amount of resources are wasted
at runtime that prolongs application execution and decrease
energy efﬁciency.
Hybrid Analysis: The ultimate aim of hybrid approaches
[105] is to increase performance and efﬁciency of augmen-
tation methods. Deciding on how to perform the ofﬂoading
mainly depends on the native resources, remote resources, and
available network bandwidth. In [105], prior to the application
execution, the system decides based on four options, namely i)
no action, ii) dynamic, iii) static, and iv) proﬁle only whether
to ofﬂoad or not and in case of ofﬂoading specify what type
of partitioning should take place. The proﬁle only option is
similar to the no action, but the systems collect execution
information to maintain execution history for future purpose.
• Remote Storage: Remote storage is the process of ex-
panding storage capability of mobile devices using remote
storage resources. It enables maintaining applications and
data outside the mobile devices and provides remote access
to them. In early efforts, researchers in [97] utilize iSCSI
(Internet Small Computer System Interface) [106] —as a
well-established protocol for remote storage —to access the
server’s I/O resources via mobile clients over the TCP/IP
network to store, backup, and mirror data [107]. However,
the throughput of iSCSI is highly affected by the mobile-
server distance. Using iSCSI is also difﬁcult for handling
large ﬁles such as multimedia and database ﬁles. Moreover,
due to message passing in wireless medium through TCP/IP,
the security and processing overhead (e.g., cryptography and
data compression) are further challenges. To alleviate these
challenges, several researches as MiSC [108], UbiqStor [109],
[110], and Intermediate Target [111] are proposed towards
realizing remote storage on mobile devices. However, due to
scalability, availability, performance, and efﬁciency issues of
traditional servers, power of remote storage could not fully
unleash using traditional servers.
Several proposals and data storage services in academia
and industry aim to expand mobile storage by exploiting
cloud computing, especially Jupiter [31], SmartBox [112],
Amazon S310
, Mozy11
, Google Docs12
, and DropBox13
. For
instance, Jupiter expands smartphone storage and assists end-
users in organizing large applications and data. Jupiter lever-
10http://aws.amazon.com/s3/
11http://mozy.com
12https://docs.google.com/
13https://www.dropbox.com/

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IEEE COMMUNICATIONS SURVEYS & TUTORIALS, ACCEPTED FOR PUBLICATION 9
ages cloud infrastructures to store big data of mobile users.
Heavy applications are executed inside the cloud’s VM of
smartphones and results are forwarded to the physical device
after execution. Amazon S3 is a general purpose storage
offering simple operations to store and retrieve cloud data
while Mozy provides data backup facilities with main focus
on enhancing cloud data safety against natural disasters.
• Resource-Aware Computing: In resource-aware comput-
ing efforts, especially [99], [100], [113]–[115], resource re-
quirements of mobile applications are diminished utilizing
the application-level resource management methods (using
application management software such as compiler and OS)
and lightweight protocols. Resource conservation is performed
via efﬁcient selection of available execution approaches
and technologies [114]. Any mobile application consists of
application-level resource management method is considered
as a resource-aware application. For instance, in [100], authors
propose an energy-friendly scheme for content-based image
retrieval applications using three ofﬂoading options, namely
i) local extraction-remote search, ii) remote extraction-remote
search, and iii) remote extraction-local search. The authors
consider available bandwidth, image database size, and num-
ber of user queries to opt any of three ofﬂoading options
for saving energy. In a high bandwidth network with limited
queries, the third option is beneﬁcial; the system uploads all
un-indexed images to the remote server and receives the results
to be loaded into the memory. Then, all search queries are
executed locally.
Similarly, applications can decide whether to choose 2G or
3G in telephony and FTP. Using 2G network for telephony and
3G for FTP can noticeably reduce resource requirements of
the mobile applications, according to the power consumption
patterns presented in [116]. 2G network technology consumes
less energy for establishing a telephony communication, while
3G is more energy-friendly for ﬁle transfer transactions.
• Fidelity adaptation: Fidelity adaptation is an alternative
solution to augment mobile devices in the absence of remote
resources and online connectivity. In this method local re-
sources are conserved by decreasing quality of application
execution, which is unlikely desirable to end-users. As a
well-known ﬁdelity adaptation approach, we can refer to
the YouTube14
. Users in YouTube can adjust the streaming
quality based on available bandwidth. To achieve optimized
performance, researchers [78], [117] leverage composition of
cyber foraging and ﬁdelity adaptation.
• Multi-tier Programming: Developing distributed multi-
tier mobile applications leveraging remote infrastructures is
another technique employed in efforts such as [36], [45], [46],
[118] to reduce resource requirements of mobile applications.
The main idea in this type of mobile applications is to
reduce the client-side computing workload and develop the
applications with less native resource requirements. Certainly,
the computationally intensive components of the applications
are executed outside the device, whereas the interactive (user
interface) and native codes (e.g., accessing to the device
camera) remain inside the device for execution.
14http://youtube.com
Multi-tier applications are lightweight aiming to consume
the least possible local resources by utilizing remote compo-
nents and services, whereas native applications are monolithic
applications often require runtime migration for execution.
Therefore, monitoring time and communication overhead of
multi-tier applications are shrunk leading to explicit resource
saving and user experience enhancement.
• Live Cloud Streaming: In recent efforts to harness cloud re-
sources, researchers from Onlive15
and Gaikai16
, among other
organizations introduce new approach to augment computing
capabilities of mobile devices, entitled live cloud streaming
[98]. In live cloud streaming approaches, mobile device acts
as a dump client able to interact with server using a browser
or application GUI. In live cloud streaming applications, entire
processing take place in the cloud and results are streaming
to the mobile devices. However, usability of cloud-streaming
is hindered by latency, network bandwidth, portability, and
network trafﬁc cost.
Functionality of cloud-streaming applications absolutely de-
pends on the network availability and the Internet. Transferring
mobile-user input to the server is another critical factor that
requires considerable attention under wireless Internet connec-
tion. Moreover, since majority of mobile network providers
deploy ‘pay-as-you-use’ data plans, the large data trafﬁc of
cloud-streaming services imposes high communication cost
on users. Yet congestion handling remains an open issue at
peak hours. Entirely relying on cloud-streaming infrastructures
and avoiding smartphones resources’ utilization impact on
application responsiveness and levy extravagant ownership,
maintenance, power, and networking expenses to the cloud-
streaming service providers, which is not a green computing
approach.
III. IMPACTS OF CMA ON MOBILE COMPUTING
This Section discusses the advantages and disadvantages
of performing a CMA process on mobile computing that
are summarized in Table IV. We aim to demonstrate how
CMA approaches mitigate deﬁciencies of mobile computing
explained in Section II-B. In this Section the terms ‘cloud
resources’ and ‘cloud infrastructures’ refer to any type of
cloud-based resources and infrastructures discussed in Section
V.
A. Advantages
In this part, eight major beneﬁts of utilizing cloud resources
in mobile augmentation processes are introduced.
1) Empowered Processing: Empowering processing is the
state of virtually increased transaction execution per second
and extended main memory leveraging CMA approaches. In
computing-intensive mobile applications, either the hosting
device does not have enough processing power and memory
or cannot provide required energy. A common solution is to
ofﬂoad the application —in whole or part —to a reliable,
powerful resource with least energy and time cost. In compu-
tation ofﬂoading, the complex, CPU- and memory-intensive
15http://onlive.com
16http://gaikai.com

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IEEE COMMUNICATIONS SURVEYS & TUTORIALS, ACCEPTED FOR PUBLICATION 10
components of a standalone application are migrated to the
cloud. Consequently, the mobile devices can virtually perform
and actually deliver the results of heavy transactions beyond
their native capabilities.
Although surrogates in traditional augmentation approaches
[8]–[10], [12] could increase computing capabilities of mobile
devices, excessive overhead of arbitrary service interruption
and denial could shadow augmentation beneﬁts [19], [119].
Cloud resources guarantee highest possible resource availabil-
ity and reliability.
Leveraging CMA approaches, application developers build
mobile application with no consideration on available native
resources of mobile devices and mobile users dismiss their
devices’ inabilities. Hence, computing- and memory-intensive
mobile applications like content-based image retrieval appli-
cations (enable mobile users to retrieve an image from the
database) can be executed on smartphones without excess
efforts.
However, a ﬂexible and generic CMA approach that can
enhance plethora of mobile devices with least conﬁguration,
processing overhead, and latency is a vital need in excessively
diverse mobile computing domain. Such diversity is mainly
due to the rapid development of smartphones and Tablets,
and sharp rise in their hardware, platform, API, feature, and
network heterogeneity [120] in the absence of early standard-
ization.
2) Prolonged Battery: Long-lasting battery can be con-
sidered as one the most signiﬁcant achievements of CMA
approaches for large number of mobile users. Smartphone
manufacturers have already utilized high speed, multi-core
ARM processors (e.g., Cortex-A57 Processor17
) being able to
perform daily computing needs of mobile end-users. How-
ever, such giant processing entities consume large energy
and quickly drain the battery that irks end-users. CMA so-
lutions can noticeably save energy [95] by migrating heavy
and energy-intensive computing to the cloud for execution.
Although energy efﬁciency is one of the most important
challenges of current CMA systems, several efforts such as
[53]–[55], [121], [122] are endeavoring to comprehend the
energy implications of exploiting cloud-based resources from
mobile devices and shrinking their energy overhead.
In traditional cyber foraging or surrogate computing ap-
proaches, energy is saved by computation ofﬂoading, but
several issues such as lack of mobility support and resource
elasticity can neutralize the beneﬁts of energy-hungry task
ofﬂoading.
3) Expanded Storage: Inﬁnite cloud storage accessible
from smartphones enables users to utilize large number of
applications and digital data on device. Hence, they are not
obliged to frequently install and remove popular applications
and data due to the space limit. Online connectivity is essential
to access cloud storage. In such online storage systems, data
are manually or automatically updated to the online storage
for maintaining the consistency of the online storage system.
Storing applications in cloud storage provides the opportu-
nity to update the code without consuming any mobile I/O
17http://www.arm.com/products/processors/cortex-a50/cortex-a57-
processor.php
transactions which enhances user experience and improves the
smartphones’ energy efﬁciency —because I/O transactions are
energy-hungry tasks.
4) Increased Data Safety: CMA efforts can bring the
beneﬁt of data safety to the mobile users. Naturally, stored
data on mobile devices are susceptible to loss, robbery,
physical damage, and device malfunction. Storing sensitive
and personal data such as online banking information, online
credentials, and customer related information on such a risky
storage signiﬁcantly degrades the quality of user experience
and hinders usability of mobile devices. Due to the scarce
computing resources, especially energy in mobile devices,
performing complex and secure encryption provisions is not
feasible. Hence, by storing data in a reliable cloud storage
[112], [123], users ensure data availability and safety regard-
less of time, place, and unforeseen mishaps. Threats such as
device robbery or physical damage to the mobile devices will
effect on the tangible value of the device rather than intangible
value of the data.
5) Ubiquitous Data Access and Content Sharing: Cloud
infrastructures play a vital role in enhancing data access
quality. Storing data in cloud resources enables mobile users
to access their digital contents anytime, anywhere, from any
device. Hence, the impact of temporal, geographical, and
physical differences is noticeably decreased that enriches user
experience.
Moreover, cloud storages facilitate data sharing and contri-
bution among authorized users. Every ﬁle and folder in cloud,
usually has a protected unique access link that can be obtained
by the owner to share them among legitimate users. Network
trafﬁc is hence, shrunk because data is accumulated in a central
server accessible to unlimited users from various machines.
Cloud can signiﬁcantly enhance data transfer among different
mobile devices. One of the most irksome user’s impediments
is to transfer data from current mobile device to a new handset.
Apart from its temporal cost, porting data from one device to
another, especially to a heterogeneous device is a risky practice
that puts data is in the risk of corruption and loss of integrity.
Stored data on Cloud remain safe and can be synchronized to
any number of mobile devices with minimum risk. However,
a reliable data access control mechanism is required to adjust
user permissions.
6) Protected Ofﬂoaded Content: Cloud storage solutions
aim to protect remote codes and data while ensure user’s
privacy. This is one of the most important gains of replacing
surrogates with cloud resources. Cloud servers deploy virtu-
alization technology to isolate the guest environment from
other guests and also from their permanent software stack.
Moreover, cloud vendors deploy strict security and privacy
policies to not only ensure conﬁdentiality of user content,
but also to protect their properties and business. Implement
internal security provisions particularly the state-of-the-art bio-
metric security systems to protect their physical infrastructures
and avoid unauthorized access. Employing complex content
encryption, frequent patching, and continuous virus signature
update inside the company premise or seeking technical ser-
vices from a trusted third party [124] are other examples of
security provisions undertaken in cloud to further protect cloud

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IEEE COMMUNICATIONS SURVEYS & TUTORIALS, ACCEPTED FOR PUBLICATION 11
TABLE IV
IMPACT OF CMA APPROACHES IN MOBILE COMPUTING.
Advantages Disadvantages
Empowered Processing Dependency to High Performance Networking Infrastructure
Prolonged Battery Excessive Communication Overhead and Trafﬁc
Expanded Storage Unauthorized Access to ofﬂoaded Data
Increased Data Safety Application Development Complexity
Ubiquitous Data Access Paid Infrastructures
and Content Sharing
Protected Ofﬂoaded Contents Inconsistent Cloud Policies and Restrictions
Enriched User Interface Service Negotiation and Control
Enhanced Application Generation Nil
storage.
7) Enriched User Interface: As described in II-B4, vi-
sualization shortcomings of mobile devices diminish user
experience and hinder smartphones’ usability. However, cloud
resources can be exploited to perform intensive 2D or 3D
screen rendering. The ﬁnal screen image can be prepared based
on the smartphone screen size and streamed to the device.
Consequently, screen adaptation also is achieved when cloud
side processing engine automatically alter the presentation
technique to match screen image with the device screen size.
8) Enhanced Application Generation: Cloud resources and
cloud-based application development frameworks similar to
µCloud and CMH, facilitate application generations in het-
erogeneous mobile environment. Once a cloud component is
built, it can be utilized to develop various distributed mobile
applications for large number of dissimilar mobile devices. In
the presence of cloud components, application programmer
needs to develop native mobile components and integrate
them with relevant, prefabricated cloud components to develop
a complex application. When a mobile-cloud application is
developed for Android device, by slightly changing native
components the application is transited to new OS like iOS18
and Symbian19
which signiﬁcantly save time and money.
B. Disadvantages
Despite of many advantageous aspects of cloud services,
their success is hindered by several drawbacks and shortcom-
ings that are discussed as follows.
1) Dependency to High Performance Networking Infras-
tructure: CMA approaches demand converged wired and
wireless networking infrastructures and technologies to fulﬁll
intersystem communication requirements. In wireless domain,
CMAs need high performance, robust, reliable, high band-
width wireless communication to realize the vision of com-
puting anywhere, anytime, from any-device. In wired commu-
nication, fast reliable communications ground is essential to
facilitate live migration of heavy data and computations to a
regional cloud-based resources near the mobile users. Efforts
such as next generation wireless networks [125] and the open
mobile infrastructure [126] with Open Wireless Architecture
18http://www.apple.com/ios/
19http://licensing.symbian.org/
(OWA) by Sieneon [127] contribute toward enhancing the
networking infrastructures’ performance in MCC.
2) Excessive Communication Overhead and Trafﬁc: Mobile
data trafﬁc is signiﬁcantly growing by ever-increasing mo-
bile user demands for exploiting cloud-based computational
resources. Data storage/retrieval, application ofﬂoading, and
live VM migration are example of CMA operations that
drastically increase trafﬁc leading to excessive congestion
and packet loss. Thus, managing such overwhelming trafﬁc
and congestion via wireless medium becomes challenging,
especially when ofﬂoading mobile data are distributed among
helping nodes to commute to/from the cloud. Consequently,
application functionality and performance decrease leading to
user experience degradation.
3) Unauthorized Access to Ofﬂoaded Data: Since cloud
clients have no control over their remote data, users contents
are in risk of being accessed and altered by unauthorized
parties. Migrating sensitive codes as well as ﬁnancial and
enterprise data to publicly accessible cloud resources decreases
users privacy, especially enterprise users. Moreover, storing
business data in the cloud is likely increasing the chance
of leakage to the competitor ﬁrm. Hence, users, especially
enterprise users hesitate to leverage cloud services to augment
their smartphones.
4) Application Development Complexity: The excessive
complexity created by the heterogeneous cloud environment
increases environment’s dynamism and complicates mobile
application development. Mobile application developers are
required to acquire extensive knowledge of cloud platforms
(i.e., cloud OSs, programming languages, and data structures)
to integrate cloud infrastructures to the plethora of mobile
devices. Understanding and alleviating such complexity im-
pose temporal and ﬁnancial costs on application developers
and decrease success of CMA-based mobile applications.
5) Paid Infrastructures: Unlike the free surrogate resources,
utilizing cloud infrastructure levies ﬁnancial charges to the
end-users. Mobile users pay for consumed infrastructures
according to the SLA negotiated with cloud vendor. In certain
scenarios, users prefer local execution or application termina-
tion because of monetary cloud infrastructures cost. However,
user payment is an incentive for cloud vendors to maintain
their services and deliver reliable, robust, and secure services
to the mobile users.
In addition, cloud vendors often charge mobile users twice;

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IEEE COMMUNICATIONS SURVEYS & TUTORIALS, ACCEPTED FOR PUBLICATION 12
Fig. 4. Taxonomy of Cloud-based Computing Resources.
once for ofﬂoading contents to the cloud and once again when
users decide to transfer their cloud data to another cloud
vendors to utilize more appropriate service (e.g., monetary and
QoS (Quality of Service) aspects).
6) Inconsistent Cloud Policies and Restrictions: One of the
challenges in utilizing cloud resources for augmenting mobile
devices is the possibility of changes in policies and restrictions
imposed by the cloud vendors. Cloud service providers apply
certain policies to restrain service quality to a desired level by
applying speciﬁc limitations via their intermediate applications
like Google App Engine bulk loader20
. Services are controlled
and balanced while accurate bills will be provided based on
utilized resources.
Also, service provisioning, controlling, balancing, and
billing are often matched with the requirements of desktop
clients rather than mobile users [128]. Considering the great
differences in wired and wireless communications, disregard-
ing mobility and resource limitations of mobile users in
design and maintenance of cloud can signiﬁcantly impact on
feasibility of CMA approaches. Hence, it is essential to amend
restriction rules and policies to meet MCC users requirements
and realize intense mobile computing on the go.
7) Service Negotiation and Control: While cloud users are
required to negotiate and comply with the cloud terms and
conditions for a certain period of time, often cloud agreements
are nonnegotiable and policies might change over the time.
Moreover, there is no control over the cloud performance and
commitments in the absence of a controlling authority or a
trusted third party. Hence, CMA services are always volatile
to the service quality of cloud vendors.
IV. TAXONOMY OF CLOUD-BASED COMPUTING
RESOURCES
Researchers [24]–[27], [27]–[43], [45]–[49] aim to obtain
user requirements and preferences by exploiting varied types
20https://developers.google.com/appengine/docs/python/tools/uploading
data
of cloud-based resources to augment computing capabilities
of resource-constraint smartphones. Based on the distance
and mobility traits of such varied cloud-based computing
resources, we classify them into four groups, namely distant
immobile clouds, proximate immobile computing entities,
proximate mobile computing entities, and hybrid that are
taxonomized in Figure 4 and explained as follows. Table V
represents the comparison results of these cloud-based com-
puting resources. This Table can be utilized as a guideline for
appropriate selection of cloud-based infrastructures in future
CMA researches.
A. Distant Immobile Clouds
Public and private clouds comprised of large number of
stationary servers located in vendors or enterprises premises
are classiﬁed in this category. They are highly available,
scalable, and elastic resources that are often located far from
the mobile nodes accessible via the Internet. Although public
cloud resources are likely more secure compared to the other
types of resources due to complex security provisions and
on-premise infrastructures [129]–[132], they are vulnerable
to security attacks and breaches like Amazon EC2 crash
[92] and Microsoft Azure security glitch [133]. Accessing
cloud resources, especially public clouds often carries the
risk of communicating through the risky channel of Internet.
However, giant clouds are endeavoring to maintain security
-for more market share- and could establish high reputation-
based trust by providing long-term services to the users.
Additionally, the performance and efﬁcacy of these ap-
proaches are affected by long WAN latency due to the long
distance between mobile client and stationary cloud data cen-
ters. One potential approach to shorten the distance between
mobile device and cloud is to migrate the remote code and
data to the computing resources near to the mobile device
via live migration of the VM from the cloud [134]. However,
live migration of VM is a non-trivial task that requires great
deal of research and development, particularly in networking
environment due to several issues such as large VM size,

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IEEE COMMUNICATIONS SURVEYS & TUTORIALS, ACCEPTED FOR PUBLICATION 13
TABLE V
COMPARISON OF CLOUD-BASED SERVERS.
Distant clouds Proximate immobile Proximate mobile Hybrid
computing entities computing entities
Architecture Distributed
Ownership Service provider Public Individual Hybrid
Environment Vendor Premise Business Center Urban Area Hybrid
Availability High Medium Medium High
Scalability High Medium Medium High
Sensing Capabilities Medium Low High High
Utilization Cost Pay-As-You-Use
Computing Heterogeneity High Medium High High
Computing Flexibility High Medium High High
Power Efﬁciency High Medium Medium High
Execution Performance High Medium Medium High
Security and Trust High Moderate Low High
Utilization Rate High
Execution Platform VM VM Physical/VM Physical/VM
Resource Intensity High Moderate Moderate Rich
Complexity Low Moderate Moderate High
Communication Technology 3G/WiFi WiFi WiFi 3G/WiFi
Communication Latency High Low Low Moderate
Execution Latency Low Medium Medium Low
Maintenance Complexity Low Medium Medium High
hard-to-predict user mobility pattern, and limited, intermittent
wireless bandwidth.
Resource utilization is enhanced in clouds due to the virtual-
ization technology deployment. Several VMs can be executed
on a single host to increase the utilization efﬁciency of the
clouds, while each computation task runs on a single isolated
VM loaded on a physical machine. However, VM security
attacks such as VM hopping and VM escape [135] can violate
the code and data security. VM hopping is a virtualization
threat to exploit a VM as a client and attack other VM(s) on
the same host. VM escape is the state of compromising the
security of the hypervisor and control all the VMs.
B. Proximate Immobile Computing Entities
The second type of cloud-based computing resources in-
volves stationary computers located in the public places near
the mobile nodes. The number of computers in public places
such as shopping malls, cinema halls, airports, and coffee
shops is rapidly increasing. These machines are hardly per-
forming tense computational tasks and are mostly playing mu-
sic, showing advertisement, or performing lightweight appli-
cations. Moreover, they are connected to the power socket and
wired Internet. Therefore, it is feasible to leverage such abun-
dant resources in vicinity and perform extensive computation
on behalf of resource-constraint mobile devices. It can also
reduce latency and wireless network trafﬁc while increases
resource utilization toward green computing. Another group of
proximate immobile computers are Mobile Network Operators
(MNO) and their authorized dealers scattered in urban and
rural areas, private clouds, and public computing kiosk [136]
that can be exploited in smartphone augmentation.
However, protecting security and privacy of mobile user and
computer owner hinder utilization of such nearby resources.
Several shortcomings such as insufﬁcient on-premise security
infrastructure, lake of tight security mechanisms, and inef-
ﬁcient update and maintenance procedures inhibit utilizing
such resources (except MNOs) for CMA approaches. Owners
of these resources may attack mobile users and access their
private data on the mobile devices or falsify ofﬂoading results.
Also, malicious users may leverage these resources as an
attacking point to violate mobile users’ security and privacy.
On the other hand, security and privacy of resource owners
are also susceptible to violation. Owners of computer devices
participating in resource sharing require robust mechanisms
to protect and isolate the guest code and data from their
host applications and data. Virtualization aims to realize such
isolation mechanism, but issues such as VM hopping and VM
escape require to be addressed before its successful adoption
[135]. Among all proximate immobile resources, MNOs may
be considered unique in terms of security and privacy features.
MNOs, in general, have been serving mobile users for long
time and could establish high degree of trust among mobile
users. It is feasible to assume that MNO’s certiﬁed dealers also
can inherit MNO’s trust if central management and monitoring
process is undertaken by MNOs [46].
C. Proximate Mobile Computing Entities
In this category of cloud-based infrastructures, various
mobile devices, particularly smartphones, Tablets, notebooks,
wearable computers, and handheld computing devices play
the role of servers based on cloud computing principles. The
main beneﬁt of utilizing nearby mobile resources is their

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Fig. 5. The Hybrid Cloud Concept for MCC.
proximity to the mobile clients. Also, hardware and platform
heterogeneity [51] between mobile servers and clients can be
mitigated, because both sides are mainly ARM-based devices
with mobile OSs. Moreover, contemporary smartphones are
able to provide value added context- and social-aware ser-
vices [137], [138] that contribute to the context-awareness
of mobile applications. However, mobile devices’ resources
are limited and they are unable to perform intensive context-
computing [139]. Realizing distributed computing on cluster
of nearby mobile devices requires several issues, particularly
application architectures, resource scheduling, and mobility to
be addressed.
Moreover, security and privacy of mobile devices as a
service provider is a critical concern in CMA. Mobile devices
are intrinsically susceptible to loss and robbery, and their con-
straint resources inhibit exploiting robust security mechanisms
inside the device. Furthermore, with ever-increasing popularity
of mobile Apps (i.e., mobile applications) in online App stores
such as Google Play21
and Samsung APPs22
[140] number
of mobile security threats are rising sharply and malware-
contaminated Apps are becoming serious threats to the mobile
users [141]. Several security threats have been identiﬁed in an
experiment of Android mobile applications with the potential
to violate the security of mobile users [142]. Risk of such
contaminated codes can likely be transferred to the non-
contaminated mobile devices by utilizing their computation
resources and request for results of a remote computation.
Hence, establishing trust between mobile devices and end-
users becomes a challenging task.
21https://play.google.com/store
22http://www.samsungapps.com
D. Hybrid (Converged Proximate and Distant Computing En-
tities)
Hybrid infrastructures as depicted in Figure 5 are comprised
of various proximate and distant computing nodes, either
mobile or immobile. The main idea behind building hybrid
resources is to employ heterogeneous computing resources to
create a balance between user requirements (mainly latency
and computation power) and available options [143]. The
latency sensitive codes are ofﬂoaded to the nearest computing
device(s) whereas the most intensive and least latency sensitive
tasks are migrated to the furthest resources. Perhaps, the
utilization costs of nearby resources are more than the remote
servers.
Beneﬁcial characteristics of hybrid resources summarized
in Table V advocates their usefulness in maximizing the aug-
mentation beneﬁts. However, deployment, management, and
resource scheduling processes in dynamic mobile environment
are non-trivial tasks. Developing an autonomic management
system similar to CometCloud [144] in cloud computing and
MAPCloud [143] in MCC to automatically manage, optimize,
and adapt hybrid infrastructures in the cloud-mobile applica-
tions can signiﬁcantly improve the quality of hybrid CMA
approaches.
Hybrid cloud infrastructures can deliver enhanced security
and privacy features to the CMA approaches and increase the
QoS. Hybrid clouds are comprised of resources with varied
security, privacy, and trust features which can be efﬁciently
utilized by CMA and mobile users as a trade-off. For instance,
security sensitive computations can perform a security-latency
trade-off and execute computation inside a secure distant
cloud.
V. THE STATE-OF-THE-ART CMA APPROACHES:
TAXONOMY
Cloud-based Mobile Augmentation (CMA) is the-state-of-
the-art mobile augmentation model that leverages cloud com-

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Fig. 6. Taxonomy of State-of-the-art CMA Models.
puting technologies and principles to increase, enhance, and
optimize computing capabilities of mobile devices by exe-
cuting resource-intensive mobile application components in
the resource-rich cloud-based resources. According to our
resource classiﬁcations in previous Section, we analyze and
taxonomize the state-of-the-art CMA approaches into four
models, namely distant ﬁxed, proximate ﬁxed, proximate
mobile, and hybrid which are depicted in Figure 6. For
each model, we describe few CMA efforts and tabulate the
comparison results in Figure 10.
A. Distant Fixed
Majority of CMA approaches [25], [27], [29], [31], [33]–
[35], [41], [43], [44], [54], [145] leverage ﬁxed cloud in-
frastructures in distance due to its straightforward approach.
Utilizing stationary cloud eliminates several management com-
plexities (e.g., resource discovery and scheduling for mobile
cloud-based servers) and alleviates reliability and security
concerns [18]. Works in this class of CMA systems aim
at reducing the complexity and overhead of utilizing distant
cloud. For instance, in [54] authors propose an energy-efﬁcient
ofﬂine job scheduling model based on makespan minimization
model to enhance energy efﬁciency of distant ﬁxed CMA
systems. Their main notion is to separate the data transmission
from the job execution. During their work, authors provide
several optimization solutions aiming to reduce the energy
consumption of the device during the ofﬂoading process.
However, for the sake of simplicity, the authors study the
energy consumption of tasks in ofﬂine mode only which does
not consider runtime dynamism of MCC.
Exploiting cloud resources is feasible in several real sce-
narios such as live cloud streaming [98], enterprise appli-
cations (e.g., Customer Relation Management (CRM) and
enterprise resource planning [146]), and Social Networking.
Cloud streaming mechanism has already described in II-C as
an example of utilizing distance ﬁxed resources. In [146],
researchers leverage cloud resources in developing a CRM
application to enhance efﬁciency of sale representatives for a
pharmaceutical company. The representative meets the physi-
cian in medical centers to promote drugs, present samples and
promotions material, and he records all sale results and details
through the mobile application. The huge database of the
company is stored inside the cloud and the sale representative
can request to process, get, or update data in database without
storing data locally.
We describe some of the distant ﬁxed CMA approaches that
utilize distant ﬁxed cloud resources for mobile augmentation
as follows. The terms immobile, ﬁxed, and stationary are
interchangeably used with the same notion.
• CloneCloud: CloneCloud [34] is a cloud-based, ﬁne-
grained, thread-level, application partitioner and execu-
tion runtime that clones entire mobile platform into the
cloud VM and runs the mobile application inside the VM
without performing any change in the application code.
The CloneCloud enables local execution of remaining
mobile application when remote server is running the
intensive components unless local execution tries to ac-
cessing the shared memory state. Cloud resources in this
effort simulate distributed execution of a monolithic ap-
plication in a resourceful environment without engaging
application developer into the distributed application pro-
gramming domain. CloneCloud can signiﬁcantly reduce
the overall execution time using thread-level migration.
When the local execution reaches the intensive compo-

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nent(s), the CloneCloud system ofﬂoads the component(s)
to the cloud and continues local execution until the
application fetches data from the migrated state. The local
execution is paused until the results are returned and
integrated to the local application.
However, the communication overhead of transferring the
clone of mobile platform, application, and memory state
and frequent synchronization of the shared data between
the mobile and cloud can shrink the power of cloud.
Such overhead becomes more intense in case of heavy,
data- and communication-intensive, and tightly coupled
mobile applications where an alternative execution of
resource-intensive and lightweight components exists.
Frequent code and data encapsulation and migration, and
mobile-cloud data synchronization excessively increase
the communication trafﬁc and impact on execution time
and energy efﬁciency of the ofﬂoading.
• Elastic Application: Elastic application model [33] is a
CMA proposal leverages distant ﬁxed cloud data cen-
ter for executing resource-intensive components of the
mobile application. Authors in this model partition a
mobile application into several small components, called
weblet. Weblets are created with least dependency to each
other to increase system robustness while decrease the
communication overhead and latency. The weblet execu-
tion is dynamically conﬁgured to either perform locally
or remotely, based on the weblet’s resource intensity,
execution environment quality, and ofﬂoading objectives.
The distinctive attribute of this proposal is that application
execution can be distributed among more than one ma-
chine and cooperative results can be pushed back to the
device. To achieve such goal, multiple elasticity patterns
namely replication, splitter, and aggregator are deﬁned. In
replication pattern, multiple replicas of a single interface
are executed on multiple machines inside the cloud.
Hence, failure in one replica will not compromise the
system performance. In splitter pattern, the interface and
implementation are separated so that several weblets with
varied implementations can share a single interface. In
aggregator, the results of multiple weblets are aggregated
and pushed to the device for optimized accuracy and
efﬁcacy.
The authors endeavor to specify the execution conﬁgu-
rations (specifying where to run the weblets) at runtime
to match the requirements of the applications and users.
To enhance the overall execution performance and enrich
user experience, the system is able to run the weblets both
locally and remotely. A weblet can be executed remotely
in a low-end device while the same can be executed
locally on a high-end device.
Elastic application model pays more attention to the
user preferences by enabling different running modes of
a single application (e.g., high speed, low cost, ofﬂine
mode). However, it engages application developers to
determine weblets organization based on the functional-
ity, resource requirements, and data dependency. But, the
characteristics of the weblets are mainly inherited from
the well-known web services to decrease the programmer
burdens.
• Virtual Execution Environment(VEE): Hung et al. [28]
propose a cloud-based execution framework to ofﬂoad
and execute the intensive Android mobile applications
inside the distant cloud’s virtual execution environment.
The quality and accuracy of execution environment is
highly inﬂuenced by the comprehensiveness and accuracy
of emulated platform. This method uses a software agent
in both mobile and cloud sides to facilitate the overall
system management. The agent in mobile device initiates
VM creation and clones the entire application (even na-
tive codes and UI components) and partial data/memory
state from device to the cloud. Unlike CloneCloud, VEE
aims to reduce latency by migrating the segment of data
stack explicitly created and owned by the application to
the VM instead of copying the entire memory; cloning
the entire memory state, especially for heavy applications
signiﬁcantly increases latency and trafﬁc.
During remote execution, the system frequently synchro-
nizes the changes between device and cloud to keep
both copies updated. In order to increase the quality and
efﬁciency of remote execution in virtual environment and
avoid data input loss at application suspension stages,
the system stores input events (reading a ﬁle, capturing a
face, storing a voice) exploiting a record/replay scheme
and pseudo checkpoint methods. However, these methods
engage application developers to separate the application
state into two states, namely global and local and to
specify the global data structures. The global state con-
tains the program domain and application ﬂow, whereas
the local state contains local data structures required by
a method. Programmer usually needs to identify global
state when the application is paused. Once the application
is suspended, the global state will be loaded to avoid re-
execution and the latest Android checkpoint is applied
to the system to reﬂect all the changes made from the
last checkpoint. However, all changes, especially user
input might be lost from the last checkpoint. To record
the changes after the last checkpoint, the record/replay
mechanism is deployed by creating a pseudo checkpoint.
To create a pseudo checkpoint, the application notiﬁes
the local agent to identify the input events and record re-
quired information. Upon the application resumption, the
pseudo checkpoint is restored to restore the application
to the state prior to the suspension.
In this effort, code security inside the cloud is enhanced
by exploiting encryption and isolation approaches that
protects ofﬂoaded code from cloud vendors eavesdrop-
ping. Using probabilistic communication QoS technique,
this is aimed to provide a communication-QoS trade-
off. For instance, the control data (usually small vol-
ume) needs highest accuracy while video streaming data
(often large volume) requires less communication accu-
racy. Moreover, the authors are optimistic that offering
secondary tasks such as automatic virus scanning, data
backup, and ﬁle sharing in the virtual environment can
enhance quality of user experience.
Although this approach aims to enhance the quality of

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application execution and augment computation capabil-
ity of mobile clients and save energy, but responsiveness
in interactive applications are likely low due to remote UI
execution. Instead of migrating entire application to the
cloud, it might be more beneﬁcial to utilize some of the
local mobile resources instead of treating mobile device
as a dump client. Data passing between mobile device and
cloud for interactive applications might degrade quality
of experience, especially in low-bandwidth, intermittent
networks.
• Virtualized Screen: Virtualized screen [42] is another
example of CMA approaches that aims to move the screen
rendering process to the cloud and deliver the rendered
screen as an image to the mobile device. The authors
aim to enrich the user experience and migrate the screen
rendering tasks to the cloud with the assumption that
majority of computation- and data-intensive processing
take place in the cloud. Hence, abundant cloud resources’
exploitation simpliﬁes the CMA system architecture,
prolongs mobile battery, and enhances the interaction
and responsiveness of mobile applications toward rich
user experience. Screen virtualization technique (running
partial rendering in cloud and rest in mobile depending
on the execution context) is envisioned to optimize user
experience, especially for lightweight, high-ﬁdelity, in-
teractive mobile applications that entirely run on local
resources. Their conceptual proposal aims to enhance
visualization capability of mobile clients, mitigate the
impact of hardware and platform heterogeneity, and facil-
itate porting mobile applications to various devices (e.g.,
smartphone, laptop, and IP TV) with different screens.
To reduce the mobile-cloud data transmission, a frame-
based representation system is exploited to forward the
screen updates from the cloud to the mobile. Frame-based
representation system captures and feeds the whole screen
image to the transmission unit. This approach updates
each frame based on the previous frame stored inside
both the mobile and cloud. However, a rich interactive,
responsive GUI needs live streaming of screen images
which is impacted by communication latency. Although
the authors describe optimized screen transmission ap-
proaches to reduce the trafﬁc, the impact of computation
and communication latency is not yet clear, as this is
a preliminary proposal. Moreover, utilizing virtualized
screen method for developing lightweight mobile-cloud
application is a non-trivial task in the absence of its
programming API.
• Cloud-Mobile Hybrid (CMH) Application: Unlike appli-
cation ofﬂoading solutions, authors in this proposal [32]
introduce a new approach of utilizing cloud resources
for mobile users. In this effort, the authors propose a
novel CMH application model, in which heavy compo-
nents are developed for cloud-side execution, whereas
lightweight or native codes are developed for mobile
devices execution. CMH Applications execution does not
need proﬁling, partitioning, and ofﬂoading processes and
hence produce least computation overhead on mobile
devices. Upon successful cloud-side execution, the results
are returned back to the mobile for integrating to the
native mobile components.
However, developing CMH applications is signiﬁcantly
complex due to the interoperability and vendor lock-in
problems in clouds and fragmentation issue in mobiles
[51]. Cloud components designed for a speciﬁc cloud are
not able to move to another cloud due to underlying het-
erogeneity among clouds. Similarly, mobile components
developed for a particular platform cannot be ported to
different platforms because of heterogeneity. Yet isolating
development of mobile and cloud components creates
further versioning and integration challenges.
To mitigate the complexity of CMH application devel-
opments and facilitate portability, the authors leverage
Domain Speciﬁc Language (DSL) [147], [148]. A DSL
is a programming language with major focus on solving
problem in speciﬁc domains. MATLAB23
is a well-known
DSL-based tool for mathematicians. A parser takes a DSL
script and converts codes into an in-memory object to be
forwarded to various automatic component generators.
The system needs different code generators for various
mobile and cloud platforms. Once the mobile and cloud
components are generated, the CMH application can be
assembled for various mobile-cloud platforms. However,
utilizing DSL-based techniques requires more generaliza-
tion efforts to be beneﬁcial in developing all types of
CMH applications.
• µCloud: Similar to the CMH framework, µCloud [36] is
a modular, mobile-cloud application framework that aims
to facilitate mobile-cloud application generation, promote
application portability, minimize the development com-
plexity, and enhance ofﬂine usability in intensive mobile-
cloud applications. Fulﬁlling separation of concerns vi-
sion, skilled programmers independently develop self-
contained components which do not have any direct inter
communications with each other. Unskilled mobile users
can mash-up (assembling available components to build
complex application) these prefabricated components to
generate a complex mobile-cloud application. Cloud ven-
dors provide infrastructure and platform as cloud services
to run prefabricated cloud components. The main idea in
this proposal is to avoid local execution of the resource-
intensive components. Hence, components are identiﬁed
as cloud, mobile, and hybrid; mobile components are
executable exclusively on mobile and cloud components
are strictly developed for cloud server while hybrid com-
ponents can either run locally or remotely. Hybrid com-
ponents have either multiple implementations or a single
implementation that need a middleware for execution.
Each component has a triplet of identiﬁer, input/output
parameters, and conﬁguration.
To alleviate ofﬂine usability issue, the authors leverage
mobile-side queuing and cloud-side caching to main-
tain data in case of disconnection. Data will be trans-
ferred upon reconnection. Application is partitioned into
components and organized as a directed graph. Nodes
23http://www.mathworks.com/products/matlab/

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represent components and vertices indicate data/control
ﬂow. Application is divided into three fragments; in each
fragment, a managing unit called orchestrator executes
and maintains component’s mash-up process. The output
of each component is forwarded using the pass-by-value
semantic as an input to the subsequent component.
Unlike Elastic Application model, the design and im-
plementation of components in µCloud is statically per-
formed in early development phase. Thus, any improve-
ment in resource availability of mobile devices or envi-
ronmental enhancement (like bandwidth growth) will not
improve the overall execution of µCloud applications.
Such inﬂexibility decreases the application execution
performance and degrades the quality of user experience.
• SmartBox: Smartbox [112] is a self-management, on-
line, cloud-based storage and access management system
developed for mobile devices to expand device storage
and facilitate data access, and sharing. It is a write-
once, read many times system designed to store personal
data such as text, song, video, and movies which is
not appropriate for large scale computational datasets. In
Smartbox, mobile devices are associated with a shadow
storage to store/retrieve personal data using a unique
account. To facilitate data sharing among larger group
of end-users in ofﬁce or at home, a public storage space
is provisioned.
Smartbox exploits traditional hierarchical namespace
for smooth navigation and employs an attribute-based
method to facilitate data navigation and service query
using semantic metadata such as the publisher-provider
metadata. Data navigation and query using tiny keyboard
and small screen irk mobile users when inquiring and
navigating stored data in cloud. However, mobile users
need always-on connectivity to access online cloud data
which is not yet achieved and is unlikely to become
reality in near future.
• WhereStore: WhereStore [149] is a location-based data
store for cloud-interacting mobile devices to replicate
necessary cloud-stored mobile data on the phone. The
main notion in this effort is that users in different places
doing various activities need dissimilar types of informa-
tion. For instance, a foreign tourist in Manhattan requires
information about nearby places of interest rather than all
the country. Hence, identifying the location and caching
predicted data deemed can enhance the system efﬁciency
and user experience. However, efﬁcient prediction of
future user location and required data, and determining
the right time for caching data are challenging tasks.
• Wukong: Wukong [150] is a cloud oriented ﬁle service
for multiple mobile devices as a user-friendly and highly
available ﬁle service. The authors provision support of
heterogeneous back-end services such as FTP, Mail, and
Google Docs Service in a transparent manner leveraging a
service abstraction layer (SAL). Wukong enables appli-
cations to access cloud data without being downloaded
into the local storage of mobile device.
Authors introduce cache management and pre-fetch
mechanisms in different scenarios to increase perfor-
mance while decreasing latency. However, it cannot al-
ways reduce latency due to the bandwidth limitation and
I/O overhead. In operations with long gap between open
and read, it is beneﬁcial to pre-fetch data from cloud
to the device that signiﬁcantly improves user experience.
Data security is enhanced via an encryption module
and bandwidth is saved using a compression module.
While compression methods utilized in this proposal is
inefﬁcient for multimedia ﬁles like image and music, it
can compress text and log ﬁles noticeably.
We conclude that one of the most effective solutions to
tackle bandwidth and latency limitations in CMA ap-
proaches, especially cloud storage is to decrease the vol-
ume of data using imminent compression methods. While
various compression methods work well on speciﬁc ﬁle
types, a cognitive or adaptive compression method with
focus on multimedia ﬁles can signiﬁcantly improve the
feasibility of cloud-storage systems.
B. Proximate Fixed
Researchers have recently proposed CMA approaches in
which nearby stationary computers are utilized. Utilizing
nearby desktop computers initiates new generation of services
to the end-user via mobile device. In [26], the authors pro-
vide a real scenario in which Ron, a patient diagnosed with
Alzheimer, receives cognitive assistance using an augmented-
reality enabled wearable computer. The system consists of a
lightweight wearable computer and a head-up display such
as Google Glass24
equipped with a camera to capture the
environment and an earphone to send the feedback to the
patient. The system captures the scene and sends the image to
the nearby ﬁx computers to interpret the scene in the image
using the object or face recognition, voice synthesizer, and
context-awareness algorithms. When Ron looks at a person for
few seconds, the person’s name and some clue information
is whispered in Ron’s ear to help greeting with the person.
When he looks at his thirsty plant or hungry dog, the system
reminds Ron to irrigate the plant and feed his dog. The nearby
resources are core component of this system to provide low-
latency real-time processing to the patient. In this part, we
explain one of the most prominent proximate ﬁxed efforts as
follows.
• Cloudlet: Cloudlet [26] is a proximate immobile cloud
consists of one or several resource-rich, multi-core, Gi-
gabit Ethernet connected computer aiming to augment
neighboring mobile devices while minimizing security
risks, ofﬂoading distance (one-hop migration from mo-
bile to Cloudlet), and communication latency. Mobile
device plays the role of a thin client while the intensive
computation is entirely migrated via Wi-Fi to the nearby
Cloudlet. Although Cloudlet utilizes proximate resources,
the distant ﬁxed cloud infrastructures are also accessible
in case of Cloudlet scarcity. The authors employ a decen-
tralized, self-managed, widely-spread infrastructure built
on hardware VM technology. Cloudlet is a VM-based
24http://www.google.com/glass/start/

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Fig. 7. Cloudlet-based Resource-Rich Mobile Computing Life Cycle.
ofﬂoading system that can signiﬁcantly shrink the impact
of hardware and OS heterogeneity between mobile and
Cloudlet infrastructures.
To reduce the Cloudlet management and maintenance
costs while increasing security and privacy of both
Cloudlet host and mobile guest, a method called “tran-
sient Cloudlet customization” is deployed which uses
hardware VM technology. It enables Cloudlet customiza-
tion prior to the ofﬂoading and performs Cloudlet restora-
tion as a post-ofﬂoading cleanup process to restore the
host to its original software stake. The VM encapsulates
the entire ofﬂoaded mobile environment (data state and
code) and separates it from the host permanent software.
Hence, feasibility of deploying Cloudlet in public places
such as coffee shops, airport lounge, and shopping malls
increases.
Unlike CloneCloud and Virtual Execution Environment
efforts that migrate the entire mobile OS clone to the
cloud, Cloudlet assumes that the entire OS clone exists
and is preloaded in the host and runs on an isolated
VM. In mobile side, instead of creating the VM of
the entire mobile application and its memory stack, the
systems encapsulates a lightweight software interface of
the intensive components called VM overlay.
The VM overall ofﬂoading performance is further en-
hanced by exploiting Dynamic VM Synthesis (DVMS)
method since its performance solely depends on
the mobile-Cloudlet bandwidth and cloudlet resources.
DVMS assumes that the base VM is already available
in the target Cloudlet and user can ﬁnd the match-
ing execution environment (VM base) among silo of
nearby Cloudlets. Upon discovery and negotiation of the
Cloudlet, the DVMS ofﬂoads the VM overlay to the
infrastructure to execute launch VM (base + overlay).
Henceforth, the ofﬂoaded code starts execution in the
state it was paused. Upon completion of Cloudlet execu-
tion the VM residue is created and sent back to the mobile
device. In the Cloudlet, the VM is discarded as a post-
ofﬂoading cleanup process to restore the original Cloudlet
state. In mobile side, the results will be integrated to
the application and local execution will be resumed. To
present a clear understanding of the overall process, the
sequence diagram of Cloudlet-based resource-rich mobile
computing is depicted in Figure 7.
Despite the noticeable ofﬂoading improvements in the
Cloudlet, its success highly depends on the existence of
plethora of powerful Cloudlets containing popular mobile
platforms’ base VM. Encouraging individual owners to
deploy such Cloudlets in the absence of monetary incen-
tives is an issue that must be addressed before deployment
in real scenarios. Although energy efﬁciency, security
and privacy, and maintenance of Cloudlet are widely
acceptable, further efforts are required to protect the
overall CMA process. Moreover, few minutes ofﬂoading
latency in Cloudlet is unacceptable to users [151].
C. Proximate Mobile
Recently, several researchers [24], [45], [122], [152]–[155]
propose CMA approaches in which nearby mobile devices
lend available resources to other mobile clients for execution

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Fig. 8. MOMCC Concept.
of resource-intensive tasks in distributed manner. Utilizing
such resources can enhance user experience in several real
scenarios such as Optical Character Recognition (OCR) and
natural language processing applications. The feasibility of
utilizing nearby mobile devices is studied in [24] where Peter,
a foreign tourist, visiting a Korean exhibition and ﬁnds interest
in an exhibit, but cannot understand the Korean description.
He can take a photo of the manuscript and translate it using
the OCR application, but his device lacks enough computation
resources. Although he can exploit the Internet web services
to translate the text, the roaming cost is not affordable to him.
Hence, he leverages a CMA solution by utilizing computation
resources of nearby mobile devices to complete the task. Some
of the CMA efforts whose remote resources are proximate
mobile devices are explained as follows.
• MOMCC: Market-Oriented Mobile Cloud Computing
(MOMCC) [45] is a mobile-cloud application frame-
work based on Service Oriented Architecture (SOA)
that harnesses a cluster of nearby mobile devices to
run resource-intensive tasks. In MOMCC, mobile-cloud
applications are developed using prefabricated building
blocks called services developed by expert programmers.
Service developers can independently develop various
computation services and uploaded them to a publicly
accessible UDDI (Universal Description Discovery and
Integration) such as mobile network operators.
Services are mostly executed on large number of smart-
phones in vicinity which can share their computation
resources and earn some money. To enhance resource
availability and elasticity, distant stationary cloud re-
sources are also available if nearby resources are in-
sufﬁcient. In order to become an IaaS (Infrastructure
as a Service) provider, mobile devices register with the
UDDI and negotiate to host certain services after secure
authentication and authorization. Mobile users at runtime
contact UDDI to ﬁnd appropriate secure host in vicinity
to execute desired service on payment. The collected rev-
enue is shared between service programmer, application
developer, UDDI, and service host for promotion and
encouragement. Figure 8 depicts the MOMCC concept.
However, MOMCC is a preliminary study and its overall
performance is not yet evident. Several issues are required
to be addressed prior to its successful deployment in
real scenarios. Executing services on mobile devices is a
challenging task considering resource limitation, security,
and mobility. Also an efﬁcient business plan that can
satisfy all engaging parties in MOMCC is lacking and
demand future efforts. MOMCC can provide an income
source for mobile owners who spend couple of hundred
dollars to buy a high-end device. In addition, faulty
resource-rich mobile devices that are able to function
accurately can be utilized in MOMCC instead of being
e-waste.
• Hyrax: Hyrax [152] is another CMA approach that ex-
ploits the resources from a cluster of immobile smart-
phones in vicinity to perform intense computations.
Hyrax alleviates the frequent disconnections of mobile
servers using fault tolerance mechanism of Hadoop. Sim-
ilar to MOMCC and Cloudlet, due to resource limita-
tions of smartphone servers, the accessibility to distant
stationary clouds is also provisioned in case the nearby
smartphone resources are not sufﬁcient. However, Hyrax
does not consider mobility of mobile clients and mobile
servers. Hence, deployment of Hyrax in real scenarios
may become less realistic due to immobilization of mo-
bile nodes. Lack of incentive for mobile servers also
hinders Hyrax success.
Hyrax is a MCC platform developed based on Hadoop
[156] for Android smartphones. In developing Hyrax,
the MapReduce [157] principles are applied utilizing
Hadoop as an open source implementation of MapRe-
duce. MapReduce is a scalable, fault-tolerant program-
ming model developed to process huge dataset over a
cluster of resources. Centralized server in Hyrax runs two

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client side processes of MapReduce, namely NameNode
and JobTracker processes to coordinate the overall com-
putation process on a cluster of smartphones. In smart-
phone side, two Hadoop processes, namely DataNode
and TaskTracker are implemented as Android services
to receive computation tasks from the JobTracker. Smart-
phones are able to communicate with the server and other
smartphones via 802.11g technology.
Nevertheless, the cloud storage connectivity in Hyrax is
missing. It demands several gigabytes of local storage to
store data and computation. Hence, user cannot access
distributed data over the Internet or Ethernet. The author
utilizes the constant historical multimedia data to avoid
ﬁle sharing. Hence, it is less beneﬁcial for interactive
and event-oriented applications whose data frequently
changes over the execution and also data-intensive appli-
cations that require huge database. The overall overhead
in Hyrax is high due to the intensity of Hadoop algorithm
which runs locally on smartphones.
• Virtual Mobile Cloud Computing (VMCC): Researchers
in [24] aim to augment computing capabilities of stable
mobile devices by leveraging an ad-hoc cluster of nearby
smartphones to perform intensive computing with min-
imum latency and network trafﬁc while decreasing the
impact of hardware and platform heterogeneity. During
the ﬁrst execution, required components (proxy creation
and RPC support) are added to the application code to
be used for ofﬂoading; the modiﬁed code will remain for
future ofﬂoading. For every application, the system de-
termines the number of required mobile servers, security
and privacy requirements, and ofﬂoading overhead. The
system continuously traces the number of total mobile
servers and their geolocation to establish a peer-to-peer
communication among them. Upon decision making the
application is partitioned into small codes and transferred
to the nearby mobile nodes for execution. The results will
be reintegrated back upon completion.
However, several issues encumber VMCC’s success.
Firstly, this solution, similar to Hyrax, is not suitable
for a moving smartphone since the authors explicitly
disregard mobility trait of mobile clients. Secondly, every
computing job is sent to exactly one mobile node; so, the
ofﬂoading time and overhead will be increased when the
serving node leaves the cluster. Thirdly, the ofﬂoading
initiation might take long since the ofﬂoading’s overall
performance highly depends on the number of available
nearby nodes; insufﬁcient number of mobile nodes defers
ofﬂoading. Finally, in the absence of monetary incentive
for mobile nodes the likelihood of resource sharing
among resource-constraint mobile devices is low.
D. Hybrid
Hybrid CMA efforts are budding [46], [143], [158] to opti-
mize the overall augmentation performance and researchers are
endeavoring to seamlessly integrate various types of resources
to deliver a smooth computing experience to mobile end-
users. For instance, mCloud [159] is an imminent proposal to
integrate proximate immobile and distant stationary computing
resources. Authors are aiming to enable mobile-users to per-
form resource-intensive computation using hybrid resources
(integrated cloudlet-cloud infrastructures). Hybrid solutions
aim to provide higher QoS and richer interaction experience
to the mobile end-users of real scenarios explained in previous
parts. For instance, in the foreign tourist example, the image
can be sent to the nearby mobile device of a non-native local
resident for processing. When the processing fails due to lack
of enough resources, the picture can be forwarded to the cloud
without Peter pays high cost of international roaming (Peter
may pay local charge).
We review some of the available hybrid CMAs as follows.
• SAMI: SAMI (Service-based Arbitrated Multi-tier In-
frastructure for mobile cloud computing) [46] proposes
a multi-tier IaaS to execute resource-intensive compu-
tations and store heavy data on behalf of resource-
constraint smartphones. The hybrid cloud-based infras-
tructures of SAMI are combination of distant immobile
clouds, nearby Mobile Network Operators (MNOs), and
cluster of very close MNOs authorized dealers depicted
in Figure 9. The compound three level infrastructures aim
to increase the outsourcing ﬂexibility, augmentation per-
formance, and energy efﬁciency. The MNO’s revenue is
hiked in this proposal and energy dissipation is prevented.
Nearby dealers can be reached by Wi-Fi. MNO’s can be
accessed either directly via cellular connection or through
dealers via Wi-Fi and broadband. Connection is estab-
lished via cellular network to contact distant stationary
clouds. The cluster of nearby stationary machines (MNO
dealers located in vicinity) performs latency-sensitive
services and omits the impact of network heterogeneity.
SAMI leverages Wi-Fi technology to conserve mobile
energy because it consumes less energy compared to
the cellular networks [116]. In case of nearby resource
scarcity or end-user mobility, the service can be executed
inside the MNO via cellular network. However, if the
resources in MNO are insufﬁcient, the computation can
be performed inside the distant immobile cloud.
The resource allocation to the services is undertaken by
arbitrator entity based on several metrics, particularly
resource requirements, latency, and security requirements
of varied services. The arbitrator frequently checks and
updates the service allocation decision to ensure high
performance and avoids mismatch.
To enhance security of infrastructures, SAMI employs
comparatively reliable and trustworthy entities, namely
clouds, MNOs, and MNO trusted dealers. MNOs have
already established reputation-trust among mobile users
and can enforce a strict security provisions to establish
indirect trust between dealers and end users ensuring that
user’s security and privacy will not be violated. SAMI ap-
plication development framework facilitates deployment
of service-based platform-neutral mobile applications and
eases data interoperability in MCC due to utilization of
SOA.
However, SAMI is a conceptual framework and deploy-

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Fig. 9. SAMI: A Multi-Tier Cloud-based Infrastructure Model.
ment results are expected to advocate its feasibility. SAMI
imposes a processing overhead on MNOs due to con-
tinuous arbitration process. Deployment, management,
and maintenance costs of SAMI are also high due to
the existence of various infrastructure layers. Moreover,
though the authors discuss the monetary aspects of the
proposal, a detailed discussion of the business plan is
missing, for example in what scenario resource outsourc-
ing is affordable for the mobile application? How does
income should be divided among different entities to be
satisfactory?
• MOCHA: In MOCHA [158] authors propose a mobile-
cloudlet-cloud architecture for face recognition applica-
tion using mobile camera and hybrid infrastructures of
nearby Cloudlet and distant immobile cloud. Cloudlet
is a speciﬁc, cheap cluster of computing entities like
GPU (Graphics Processing Unit) capable of massively
processing data and transactions in parallel. Cloudlets
are able to be accessed via heterogeneous communication
technologies such as Wi-Fi, Bluetooth, and cellular. The
mobile often access processing resources via Cloudlet
rather than directly connecting to the cloud, unless ac-
cessing cloud resources bears lower latency.
Cloudlet receives the smartphones intensive computation
tasks and partitions them for distribution between it-
self and distant immobile clouds to enhance QoS [26].
MOCHA leverages two partitioning algorithms: ﬁxed
and greedy. In the ﬁxed algorithm, the task is equally
partitioned and distributed among all available computing
devices (including Cloudlet and cloud servers), whereas
in greedy algorithm, the task is partitioned and distributed
among computing devices based on their response times;
the ﬁrst partition is sent to the quickest device while the
last partition is sent to the slowest device. The response
time of the task partitioned using greedy approach is
signiﬁcantly better than ﬁxed, especially when Cloudlet
server is utilized in augmentation process and large
number of clouds with heterogeneous response time exist.
However, smartphones in MOCHA require prior knowl-
edge of the communication and computation latency of
all available computing entities (Cloudlet and all available
distant ﬁxed clouds) which is a resource-hungry and time-
consuming task.
VI. CMA PROSPECTIVES
People dependency to mobile devices is rapidly increasing
[89], [160] and smartphones have been using in several crucial
areas, particularly healthcare (tele-surgery), emergency and
disaster recovery (remote monitoring and sensing), and crowd
management to beneﬁt mankind [161]–[163]. However, intrin-
sic mobile resources and current augmentation approaches are
not matching with the current computing needs of mobile-
users, and hence, inhibit smartphone’s adoption. Upon slow
progress of hardware augmentation, the highly feasible solu-
tion to fulﬁll people computing needs is to leverage CMA
concept. This Section aims to present set of guidelines for
efﬁciency, adaptability, and performance of forthcoming CMA
solutions. We identify and explain the vital decision making
factors that signiﬁcantly enhance quality and adaptability of
future CMA solutions and describe ﬁve major performance
limitation factors. We illustrate an exemplary decision making
ﬂowchart of next generation CMA approaches.
A. CMA Decision Making Factors
These factors can be used to decide whether to perform
CMA or not and are needed at design and implementation
phases of next generation CMA approaches. We categorize
the factors into ﬁve main groups of mobile devices, contents,
augmentation environment, user preferences and requirements,
and cloud servers, which are depicted in Figure 11 and
explained as follows.
1) Mobile Devices: From the client perspective, amount
of native resources including CPU, memory, and storage is
the most important factor to perform augmentation. Also,
energy is considered a critical resource in the absence of long-
spanning batteries. The trade-off between energy consumed by
augmentation and energy squandered by communication is a
vital proportion in CMA approaches [73]. Device mobility and
communication ability (supporting varied technologies such
as 2G,3G,Wi-Fi) are other metrics that are important in the
ofﬂoading performance.
2) Contents: Another inﬂuential factor for CMA decision
making is the contents’ nature. The code granularity and
size as well as data type and volume are example attributes
of contents that highly impact on the overall augmentation
process. Hence, the augmentation should be performed con-
sidering the nature and complexity of application and data.

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Fig. 10. Comparison of CMA Approaches.
For instance, latency sensitive small data are efﬁcient to be
processed locally, whereas sensitive big data are encouraged to
be stored in a large reliable cloud storage. Similarly, ofﬂoading
a coarse-grained, large code to a distant ﬁxed cloud via a low
bandwidth network is not feasible.
3) Augmentation Environment: Mobile computing is a het-
erogeneous environment comprised of non-uniform mobile
nodes, communication technologies, and resources. One of the
most inﬂuential environment-dependent factors is the wireless
communication medium in which majority of communications
take place. Wireless is an intermittent, unreliable, risky, and
blipping medium with signiﬁcant impact on the quality of
augmentation solutions. The overall performance of a low cost,
highly available, and scalable CMA approach is magniﬁcently
shrunk by the low quality of communication medium and
technologies. Selecting the most suitable technology consider-
ing the factors like required bandwidth, congestion, utilization
costs, and latency [164] is a challenge that affects quality
of augmentation approaches in wireless domains. Wireless
medium characteristics impose restrictions when specifying
remote servers at design time and runtime.
Moreover, dynamism and rapidly changing attributes of the
runtime environment noticeably impact on augmentation pro-
cess and increase decision making complexity. Augmentation
approaches should be agile in dynamic mobile environment
and instantaneously reﬂect to any change. For example, user
movement from high bandwidth to a low bandwidth network,
receding from the network access point, and rapidly changing
available computing resources complicate CMA process.
4) User Preferences and Requirements: End-users’ physi-
cal and mental situations, individual and corporate preferences,
and ultimate computing goals are important factors that affect
ofﬂoading performance. Some users are not interested to
utilize the risky channel of Internet, while others may demand
accessing cloud services through the Internet. Hence, users
should be able to modify technical and non-technical spec-
iﬁcations of the CMA system and customize it according to
their needs. For example, user should be able to alter degree of
acceptable latency against energy efﬁciency of an application
execution. Selecting the most appropriate resource among
available options can also enhance overall user experience.
5) Cloud Servers: As explained, CMA approaches can
leverage various types of cloud resources to enhance com-
puting capabilities of mobile devices. Therefore, the overall
performance and credibility of the augmentation approaches
highly depend on the cloud-based resources’ characteristics.
Performance, availability, elasticity, vulnerability to security
attacks, reliability (delivering accurate services based on
agreed terms and conditions), cost, and distance are major
characteristics of the cloud service providers used for aug-
menting mobile devices.
Utilizing clouds to augment mobile devices notably reduces
the device ownership cost by borrowing computing resources
based on pay-as-you-use principle. Such elastic, cost-effective,

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Fig. 11. Critical Factors in CMA Decision Making.
reliable, and relatively trustworthy resources are embraced by
the scholars, industrial organizations, and end-users towards
ﬂourishing CMA approaches.
B. Performance Limitation Factors
Performance of varied CMA solutions is impacted by sev-
eral factors. We describe ﬁx major performance limitation
factors as follows.
1) Heterogeneity: MCC is a highly heterogeneous envi-
ronment comprised of three diversiﬁed domains of mobile
computing, cloud computing, and networking. Although het-
erogeneity can provide ﬂexibility to the mobile users by
providing selection alternatives, it breeds several limitations
and challenges, especially for developing multi-tier CMA-
based applications [51]. Dissimilar mobile platforms such as
Android, iOS, Symbian, and RIM beside diverse hardware
characteristics of mobile device inhibit data and application
portability among varied mobile devices. Portability is the abil-
ity to migrate code and data from one device to another with
no/less modiﬁcation and change [165]. Existing heterogene-
ity in cloud computing including hardware, platform, cloud
service policy, and service heterogeneity originates challenges
such as portability and interoperability and fragment the MCC
domain.
Network heterogeneity in MCC is the composition of var-
ious wireless technologies such as Wi-Fi, 3G, and WiMAX.
Mobility among varied network environments intensiﬁes com-
munication deﬁciencies and stems complex issues like signal
handover [125]. Inappropriate decision making during the
handover process like (i) less appropriate selection of network
technology among available candidates and (ii) transferring the
communication link at the wrong time, increases WAN latency
and jitter that degrade quality of mobile cloud services. Con-
sequently delay-sensitive content and services are degraded
[166] and adoption of CMA approaches are hindered.
2) Data Volume: Ever-increasing volume of digital con-
tents [85] signiﬁcantly impacts on the performance of CMA
approaches in MCC. Current wireless infrastructures and tech-
nologies fail to efﬁciently fulﬁll the networking requirements
of CMA approaches. Storing such a huge data in a single ware-
house is often impossible and demands data partitioning and
distributed storage that not only mitigates data integrity and
consistency, but also makes data management a pivotal need
in MCC [167]. Applying a single access control mechanism
for relevant data in various storage environments is another
challenging task that impacts on the performance and adoption
of CMA solutions in MCC.
3) Round-Trip Latency: Communication and computation
latency is one of the most important performance metrics of
mobile augmentation approaches, especially when exploiting
distant cloud resources. In cellular communications, distance
from the base station (near or far) and variations in bandwidth
and speed of various wireless technologies affect the perfor-
mance of augmentation process for mobile devices. Moreover,
leveraging wireless Internet networks to ofﬂoad content to the
distant cloud resources creates a bottleneck. Latency adversely
impacts on the energy efﬁciency [73] and interactive response
[168] of CMA-based mobile applications due to excessive
consumption of mobile resources and raising transmission
delays.
Recently, researches [169], [170] are emerging toward de-
creasing the networking overhead and facilitating mobility
(both node and code mobility) in cloud-based ofﬂoading
approaches. For example, Follow-Me Cloud [169] aims at
enabling mobility of network end-points across different IP

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subnets. The authors employ the concept of identiﬁer and
locator separation of edge networks using OpenFlow-enabled
switches. Leveraging the Follow-Me Cloud, mobile nodes
can move among access networks without being notiﬁed of
any change or session disruption. All corresponding nodes
that have been communicating with the mobile node can
continue their communication without interruption. When the
node migrates, its old IP turn to identiﬁer and its new IP
address becomes locator address so that all other nodes can
keep communication with the moving node. However, for
each packet traveling to/from the mobile node, there is an
overhead of manipulating the locator/identiﬁer values. Future
improvement and optimization efforts will enhance the CMA
systems’ performance.
In cloud side, computation latency signiﬁcantly impacts on
the application responsiveness. Researchers study the impact
of cloud computation performance on the execution time
and vindicate 12X reduction in performance time violation
[171]. Thus, the increased latency degrades the quality of
user experience and adversely impacts on the user-perceived
performance of CMA solutions.
4) Context Management and Processing: Performance of
CMA approaches is noticeably degraded by lack of sufﬁcient,
accurate knowledge about the runtime environment. Contem-
porary mobile devices are capable of gathering extensive con-
text and social information such as available remote resources,
network bandwidth, weather conditions, and users’ voice and
gestures from their surrounding environment [137], [138]. But,
storing, managing, and processing large volume of context
information (considering MCC environment’s dynamism and
mobile devices’ mobility) on resource-constraint smartphones
are non-trivial tasks.
5) Service Execution and Delivery: SLA as a formal
contract between service consumer and provider enforces
resource-level QoS (e.g., memory capacity, compute unit, and
storage) against a fee, which is not sufﬁcient for mobile users
in highly dynamic wireless MCC environment. User-perceived
performance in MCC is highly affected by the quality of cloud
computations, wireless communications, and local execution.
Hence, varied service providers, including cloud vendors,
wireless network providers, and mobile hardware and OS
vendors need to collaborate and ensure acceptable level of
QoS. For successful CMA approaches, comprehensive real-
time monitoring process is expected to ensure that engaging
service vendors are delivering required services in acceptable
level based on the accepted SLA.
C. CMA Feasibility
Although CMA is beneﬁcial and can saves resources [40],
several questions need to be addressed before CMA can be
implemented in real scenarios. For instance: is CMA always
feasible and beneﬁcial? Can CMA save local resources and
enhance user experience? What kind of cloud-based resources
should be opted to achieve the superior performance?
Vision of future CMA proposals will be realized by accurate
sensing and acquiring precise knowledge of decision making
factors like user preferences and requirements, augmentation
environment, and mobile devices, which are explained in
previous part. A decision making system, similar to those used
in [25], [33], [49], analyzes these vital factors to determine
the augmentation feasibility and speciﬁes if augmentation can
fulﬁll mobile computation requirements and enrich quality
of user experience. Figure 12 illustrates a possible decision
making ﬂow of future CMA approaches.
Availability of mobile resources to manage augmentation
process and volume of cloud resources to provision required
resources signiﬁcantly impact on the quality of augmentation
[9]. Similarly, user preferences, limitations, and requirements
affect the augmentation decision making. For instance, if aug-
mentation is not permitted by users, the application execution
and data storage should be performed locally without being
ofﬂoaded to a remote server(s) or be terminated in the absence
of enough local resources. Similarly, augmentation process
can be terminated if the execution latency of delay-sensitive
content is sharply increased, quality of execution is noticeably
decreased, or security and privacy of users is violated [19].
Furthermore, usefulness of CMA approaches highly de-
pends on the execution environment. Ofﬂoading computation
and mobile-cloud communication ratio, distance from mobile
to the cloud, network technologies and coverage, available
bandwidth, trafﬁc congestion, deployment cost, and even na-
ture of augmentation tasks alter usefulness of the CMA ap-
proaches [40]. For instance, performing an ofﬂoading method
on a data-intensive application (e.g., applying a graphical ﬁlter
on large number of high quality images) in a low-bandwidth
network imposes large latency and signiﬁcantly degrades user
experience which should be avoided. Similarly, migrating a
resource-hungry code to an expensive remote resource can
be unaffordable practice. Suppose in a sample augmentation
approach RC is the total native resources consumed during
augmentation, RM is the total native resources consumed
for maintenance, and RS is the total resources conserved in
augmentation process. Explicitly for a feasible augmentation
approach RC + RM << RS. However, in some traditional
augmentation approaches, the left side of the equation exceeds
the right which is not effective in augmenting resource-poor
mobile devices [19].
VII. OPEN CHALLENGES
In this Section, we highlight some of the most important
challenges in deploying and utilizing CMA approaches as the
future research directions.
A. Reference Architecture for CMA Development
Recently, researchers leverage dissimilar structures and
techniques in utilizing cloud resources to augment compu-
tation capabilities of mobile devices. Also, different efforts
focus on varied types of mobile applications, particularly
multimedia, intense games, image processing, and workﬂow
processing applications.
Such diffusion scatters CMA development approaches and
increase adaptability challenges of CMA solutions. In the
absence of a reference architecture and uniﬁed CMA solution,
various CMA approaches need to be integrated to all mobile

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Fig. 12. An Exemplary Decision Making Flow of Future CMA Approaches.
OSs to serve multi-dimensional needs of various mobile users
which is a not-trivial task. The reference architecture is ex-
pected to be generic enough to be deployed in family of CMA
approaches.
B. Autonomic CMA
CMA approaches are drastically increasing volume of dis-
tributed mobile content in a horizontally heterogeneous mobile
cloud computing environment [120]. Exploiting heterogeneous
communication technologies to employ diverse cloud-based
infrastructures for augmenting a plethora of dissimilar mobile
devices is signiﬁcantly intensifying complexity and manage-
ment. Employing lateral solutions and controlling the mobile
phones using outside entities (e.g., third party management
systems) to deal with such complexity might further amplify
complexity. A feasible alternative to mitigate such complexity
is to develop autonomic self-managing, -healing, -optimizing,
and -protecting CMA approaches [172] able to adapt to
environment dynamism and hide complexity.
C. Application Mobility Provisioning
Enabling continuous and consistent mobility in CMA mod-
els (especially proximate mobile and hybrid) to provision
ubiquitous, convenient, on-demand network access to cloud-
based computing resources is a vital challenge. Seamless code
mobility in CMA models is more challenging compared to
the traditional augmentation approaches, because in CMA
approaches service providers and consumers can move during
the augmentation process which intensiﬁes the code mobility
[173]. Therefore, communication disruption and intermittency
can cause several challenges, especially dismissal of always-
on connectivity, excessive consumption of limited mobile
resources, and frequent interruption of application execution
which decreases quality of computing services and degrades
quality of mobile user experience [72]. Also, it levies re-
dundant costs on cloud-mobile users and inhibits reliability
of CMA models. Hence, alleviating such difﬁculties using
Web advancements [170] and imminent lightweight cognitive
mobility management systems with least signal trafﬁc and
latency can signiﬁcantly enhance the ubiquitous connectivity
and increase the positive impact of CMA.
D. Computing and Temporal Cost of Mobile Distributed Ex-
ecution
Noticeable computation and communication cost of migrat-
ing tasks from the mobile device to the remote servers and
receiving the results is another challenges of CMA approaches
in MCC, which is intensiﬁed by mobility and wireless commu-
nication constraints. Although researchers [45], [152] endeavor
to reduce the distance of mobile devices and service providers
by leveraging nearby mobile/ﬁxed computing devices, several
mechanisms, particularly resource discovery and allocation,
service consumer and provider mobility management, and
distributed runtime are required to realize the CMAs vision.
Accurate allocation of resources to the mobile computation
tasks demands comprehensive knowledge about structure and
performance features of available service providers and re-
source requirements of mobile computation tasks. Thus, QoS-
aware scheduling efforts such as [143] are necessary to en-
hance the CMA usability.
E. Seamless Communication
Maintaining a continuous communication between mobile
service consumers and mobile/ﬁxed service providers in in-
termittent heterogeneous wireless environment is a non-trivial
task. User mobility and wireless disconnection highly im-
pact on resource utilization ratio. When the mobile service
providers and consumers loss communication link due to
mobility-made prolonged distance, the service consumer re-
quires to either performing local execution or re-initiating aug-
mentation process. Similarly, the resource-constraint mobile
server consumes its scarce resources for processing an orphan
computation whose results are ineffectual after losing the
communication link. Potential solutions may transfer partially-
completed tasks to a nearby node or initiate parallel execution
on third device before disconnection, or cache results for future
references.
F. Multipoint Data Bridging
Unlike traditional ofﬂoading methods that require point-
to-point code and data migration and processing, CMA ap-
proaches require multipoint data migration and interoperation

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to achieve the maximum beneﬁts from the distributed het-
erogeneous infrastructures in mobile-cloud ecosystem. Con-
necting heterogeneous systems (based on wired or wireless),
understanding geographical information resources, and ex-
changing data between/across two or more heterogeneous
systems [174] are the main issues in CMA system which
demand arousing data interoperation techniques in multi-
domains mobile cloud environment. The inward heterogeneous
architectures and data structures of mobile devices and cloud
systems with different APIs can exemplify the intensity of
multipoint data bridging challenge [23]. Hence, ofﬂoading
computational tasks from a mobile device to a cloud, perform-
ing computational interoperation among varied clouds (for cost
and performance concerns), and pushing results to the mobile
device become challenging tasks [175]. Therefore, multipoint
data bridging in dynamic heterogeneous environment remains
as a future research direction to realize accessing, interpreting,
processing, sharing, and synchronizing distributed contents.
G. Distributed Content Management
Rapid growth in digital contents and increasing depen-
dency of mobile users to cloud infrastructures impede content
management for mobile users. Researchers distribute code
and data among heterogeneous nearby and distant resources
via different communication technologies to optimize CMA
process. Although executing complex, heavy applications and
accessing large data volume are facilitated, managing huge
volume of distributed contents for smartphone users is not
straightforward. Therefore, enabling mobile users to efﬁciently
locate, access, update, and synchronize highly distributed
contents requires future research and developments.
H. Seamless/Lightweight CMA
Developing lightweight mobile computing augmentation
approaches to increase quality of mobile user experience
and to develop CMA system independent of any particular
situation is a signiﬁcant challenge in mobile cloud environ-
ment. Ofﬂoading bulk data in limited wireless bandwidth,
and VM initiation, migration, and management in a secure
and conﬁdential manner are particular tasks in CMA system
that noticeably increase overall execution time, intensify the
augmentation latency, and decrease the quality of mobile
user experience. CMA approaches are generally hosted and
executed inside the mobile devices to conserve their local
resources and hence, need to avoid excessive resource hungry
transactions.
A feasible approach to decrease the volume of digital
contents —in limited bandwidth networks —is to utilize
effective, efﬁcient data compression methods. Available com-
pression techniques are unlikely efﬁcient considering structure
of current multimedia ﬁles. Moreover, approaches like Par-
avirtualiztion [176] as a lightweight virtualization technique
can reduce the overhead by partially emulating the OS and
hardware. Paravirtualization approaches virtualize only parts
of the hardware required for computing. Thus, the mobile-
side VM creation overhead is diminished and the impact of
VM migration on network is reduced. Therefore, realizing
lightweight CMA approaches demands lightweight computa-
tion and communication techniques (particularly in virtualiza-
tion, data compression, and encryption methods) to reduce
intra-system correspondence, data volume, and I/O tasks.
I. Security in Mobile Cloud
One of the most challenging aspects of CMA is protecting
ofﬂoaded code and data inside the cloud. While securing con-
tents inside the mobile consumes huge resources, ofﬂoading
plain contents through insecure wireless medium and storing
plain data inside the cloud highly violates user security and
privacy. Despite of the large number of research and develop-
ment in establishing trust in cloud [177]–[180], security and
privacy is still one of the major user concerns in utilizing
cloud resources that impede CMA deployment. Addressing
such crucial needs by employing a novel lightweight secu-
rity algorithm in mobile side and a set of robust security
mechanisms in cloud demand future efforts to promote CMA
among smartphone users. In privacy aspects, though recent
social behaviors of users in social websites such as Facebook
and tweeter advocates that large community of users partially
forfeit privacy, they still need certain degree of robust privacy
to protect their conﬁdential, clandestine data.
J. Live Virtual Machine Migration
Live migration of VMs between distributed cloud-based
servers (especially for distant servers) is a crucial requirement
in successfully adopting CMA solutions in MCC, considering
wireless network bandwidth and intermittency, and mobility
limitations. When a mobile user moves to a place far from
the ofﬂoaded contents (code or data), the enlarged distance
increases access latency and degrades user-observed applica-
tion performance. Hence, mobilizing the running VM along
with the mobile service consumer without perceivable service
interruption becomes essential to avoid user experience degra-
dation. However, sharp growing computation and data volume
in blipping wireless environment intensiﬁes live migration of
VM. Therefore, efforts similar to VMware vMotion [181] and
[121], [182] are necessary to optimize VM migration in MCC.
Reducing computation complexity and overhead, energy, data
volume, and communication cost are critical in low-latency
low-cost migration of VM in MCC.
Furthermore, after successful live VM migration, it is essen-
tial to ensure that migrated VM is seamlessly accessible via
initial IP address when the VM changes its physical machine.
Future efforts similar to [183], [184] and LISP [185] are
needed to realize the vision of seamless access to migrating
VM in MCC.
VIII. CONCLUSIONS
Augmenting computing capabilities of mobile devices, es-
pecially smartphones using cloud infrastructures and principles
is an emerging research area. The ultimate goal of CMA
solutions is to realize the vision of unrestricted functionality,
storage, and mobility regardless of underlying devices and
technologies’ constraints. Elasticity, availability, and security

32.
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[180] S. Ruj, A. Nayak, and I. Stojmenovic, “DACC: Distributed access
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Saeid Abolfazli is currently a Ph.D. candidate,
research assistant in High Impact Research Project
(Mobile Cloud Computing: Device and Connectiv-
ity) fully funded by Malaysian Ministry of Higher
Education, and part time lecturer in the Department
of Computer Systems and Technology at the Uni-
versity of Malaya, Malaysia. He received his M.Sc
in Information Systems in 2008 from India and BE
(Software Engineering) in 2001 from Iran. He has
been serving as CEO of Espanta Information Com-
plex during 1999-2006 in Iran. He also was part time
lecturer to the ministry of education and Khorasan Technical and Vocational
Organization between 2000 and 2006. He is a member of IEEE society
and IEEE CS Cloud Computing STC. He has been serving as a reviewer
for several international conference and ISI journals of computer science.
His main research interests include Mobile Cloud Computing, lightweight
protocols, and service oriented computing (SOC). Please write to him at
abolfazli.s@gmail.com or abolfazli@ieee.org. For further information, please
visit his cyberhome at www.mobilecloudfamily.com
Zohreh Sanaei is currently a Ph.D. candidate and
research assistant in High Impact Research Project
(Mobile Cloud Computing: Device and Connectiv-
ity) fully funded by Malaysian Ministry of Higher
Education in the Department of Computer Sys-
tems and Technology at the University of Malaya,
Malaysia. She received her M.Sc. in Information
Systems in 2008 from India and BE (Software En-
gineering) in 2001 from Iran. She worked in 3MCD
and EIC, Iran as a network engineer and participated
in several wireless communication projects from
2001 till 2006. She has been working for more than 6 years as a part-time lec-
turer for the ministry of social affairs, Iran as a technical and vocational trainer.
Her main research interests include mobile cloud computing, distributed
computing, and ubiquitous computing. She is a member of IEEE society and
can be corresponded via zsanaeim@gmail.com or sanaei@ieee.org. For further
information, please visit her cyberhome at www.mobilecloudfamily.com
Ejaz Ahmed was born in Gandhian, Mansehra,
Pakistan. He did his B.S (Computer Science) from
Allama Iqbal Open University, Islamabad, Pakistan.
Afterward, he completed his M.S (Computer Sci-
ence) from Mohammad Ali Jinnah University, Is-
lamabad in 2009. Currently, he is pursuing his
PhD Candidature under Bright Spark Program at
Faculty of Computer and Information Technology,
University Malaya, Kuala Lumpur, Malaysia. He is
an active researcher in Mobile Cloud Computing
Research Group at Faculty of Computer Science and
Information Technology, University Malaya, Kuala Lumpur, Malaysia. His
areas of interest include Seamless Application Execution Framework Design
for Mobile Cloud Computing, Designing of Channel Assignment and Routing
Algorithms for Cognitive Radio Networks.
Dr. Abdullah Gani is Associate Professor of Com-
puter System and Technology at the University
of Malaya, Malaysia. His academic qualiﬁcations
were obtained from UK’s universities - bachelor
and master degrees from the University of Hull,
and Ph.D from the University of Shefﬁeld. He has
vast teaching experience due to having worked in
various educational institutions locally and abroad -
schools, teaching college, ministry of education, and
universities. His interest in research started in 1983
when he was chosen to attend Scientiﬁc Research
course in RECSAM by the Ministry of Education, Malaysia. More than
100 academic papers have been published in conferences and respectable
journals. He actively supervises many students at all level of study - Bachelor,
Master and PhD. His interest of research includes self-organized systems,
reinforcement learning, and wireless-related networks. He is now working on
mobile cloud computing with High Impact Research Grant for the period of
2011-2016.
Dr. Rajkumar Buyya is Professor of Computer
Science and Software Engineering, Future Fellow
of the Australian Research Council, and Director
of the Cloud Computing and Distributed Systems
(CLOUDS) Laboratory at the University of Mel-
bourne, Australia. He is also serving as the founding
CEO of Manjrasoft, a spin-off company of the
University, commercializing its innovations in Cloud
Computing. He has authored over 425 publications
and four text books including “Mastering Cloud
Computing” published by McGraw Hill and Else-
vier/Morgan Kaufmann, 2013 for Indian and international markets respec-
tively. He also edited several books including “Cloud Computing: Principles
and Paradigms” (Wiley Press, USA, Feb 2011). He is one of the highly cited
authors in computer science and software engineering worldwide (h-index=70,
g-index=144, 23000+ citations). Microsoft Academic Search Index ranked
Dr. Buyya as the world’s top author in distributed and parallel computing
between 2007 and 2012. Recently, ISI has identiﬁed him as a “Highly Cited
Researcher” based on citations to his journal papers.
Software technologies for Grid and Cloud computing developed under
Dr. Buyya’s leadership have gained rapid acceptance and are in use at
several academic institutions and commercial enterprises in 40 countries
around the world. Dr. Buyya has led the establishment and development
of key community activities, including serving as foundation Chair of the
IEEE Technical Committee on Scalable Computing and ﬁve IEEE/ACM
conferences. These contributions and international research leadership of Dr.
Buyya are recognized through the award of “2009 IEEE Medal for Excellence
in Scalable Computing” from the IEEE Computer Society, USA. Manjrasoft’s
Aneka Cloud technology developed under his leadership has received “2010
Asia Paciﬁc Frost & Sullivan New Product Innovation Award” and “2011
Telstra Innovation Challenge, People’s Choice Award”. He is currently serving
as the foundation Editor-in-Chief (EiC) of IEEE Transactions on Cloud
Computing. For further information on Dr. Buyya, please visit his cyberhome:
www.buyya.com